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GROPYUS is a technology-based construction company focused on building multi-story residential buildings. Thanks to its prefabricated building system with various design options, industrial offsite construction, and fully digitalized processes, the company manufactures aspirational, sustainable, and affordable homes using timber construction methods. GROPYUS is using scalable construction and manufacturing solutions to tap into a future market, boost Europe's strength in innovation, while also playing a substantial role in improving sustainability.
Role Description We are growing our Data Language Team within the Gropyus Tech department. The Language team is responsible for the semantic layer of our Gropyus Data Fabric as well as data modeling and transformation for our self-service analytics. Our team interacts with experts from various domains such as: - Digital Building Planning and Automation - Product Operations - Sustainability - AI - IoT - Construction engineers - Building architects - Logistics experts - Software engineering As part of the Data Language organization, you will: - Design data models to formalize concepts from various architecture and construction domains. - Contribute to the logic to transform and enrich our centralized data for self-service analytics. - Support Data Science use cases including Machine Learning and AI. - Collaborate with domain experts and software engineers to understand data needs and deliver high-quality datasets. - Implement and uphold data quality, governance, and security standards, including monitoring, testing, and documentation. - Adhere to best practices and rigor in development including documentation, data governance, testing, and validation. Qualifications - Experience working with a tech stack similar to: - Programming languages like Python or Kotlin - Query Languages like SPARQL, SQL - Data Reporting like PowerBI, Tableau, Quick Sight - Databases like Postgres, BigQuery, Spark, Graph DB - Cloud Storage Platforms - Ability to complete work as directed with guidance from senior engineers or leadership. - Experience resolving issues related to data discrepancies and inconsistencies and creating validation and testing for prevention and handling. - Data modeling experience through semantic or Business Intelligence development. - Experience following best practice guidelines in data and software engineering. Requirements - Some knowledge about semantic layer or ontologies (optional). - Experience with graph technologies and triples (optional). - Data Science, Machine Learning, and AI agents (optional). Benefits - Be part of something big: Join us in reinventing construction and sustainable, affordable living. - It’s on you: We offer a tremendous amount of ownership and room to make a mark at all organization levels. - Focus on results: You choose if you work from home, a park, or the office. - Bring your uniqueness to the team: Diversity in background, experience, and thinking is crucial to create the best product for everyone. - Be an owner: Participate in the success of GROPYUS through stock options.
Role Description You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself. This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build. What makes this role different: - You're first. Governance has been everyone's side job, so what exists today is yours to reshape - keep what works, redesign what doesn't, and your standards become the company's standards. - Whole-stack ownership. Source data to pipelines to dashboards and ML models - you own trust across the entire chain, not one slice of it. - A live migration to shape. Lightdash is landing now. You get to set up its permissions, structure, and norms before bad habits form, instead of untangling them later. What You'll Own: - Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen. - Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. - Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. - The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales. - Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows. - AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer. - Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what. - The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics. Qualifications - Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy. - AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go. - A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself. - Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. - An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards. Requirements - Zero pipeline incidents from unannounced source-data changes. - Zero freshness incidents - stakeholders never open a stale dashboard. - Every area of the business manages on official, well-maintained metrics and dashboards. - Every Segment event has an owner and a standard. - Governance runs as a system - documented processes that would survive you taking a month off. Benefits - Base salary: $75k–$100k/year - Equity: The whole company makes decisions on the data you'll guard. - Fully remote: This work needs deep focus, and we trust you to manage your environment. - Flexible PTO: We focus on results. Take what you need.
We're making driverless vehicles a safe, reliable, and accessible reality.
Role Description We are seeking a highly skilled and motivated Senior Data Analysis Engineer for our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principles, and statistical analysis. At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day. The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UIs. The team works together with all other perception and ML teams to develop the metrics and analyze results. What You'll Do: - Work with ML Engineers and Autonomy Software Developers to develop new data analysis metrics and KPIs for the validation of the autonomous vehicle performance. - Drive innovation by researching and developing new large scale data analysis systems. - Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software. - Build high-quality datasets to improve ML products through training & edge case validation. - Provide statistical depth on model performance & generalization through rigorous error and change analysis. Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, or a related field. - Strong programming skills in Python, SciPy, and related data analysis frameworks. - Experience in machine learning, data mining, and statistical analysis. - Experience with large-scale data processing and distributed computing technologies. - Strong communication and software development skills: goal setting, design documentation, test frameworks, collaborative cross team development. Requirements - Ph.D. in Computer Science or a related field (Bonus Points). - Experience with cloud platforms such as AWS, Google Cloud, or Azure (Bonus Points). - Experience with machine learning in the autonomous driving domain (Bonus Points). - Publications or contributions to the AI/ML community (Bonus Points). Benefits - Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. Salary Range $159,000 — $207,000 USD Company Description Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. - Our journey is always people first. - We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. - Our team is made up of engineers, researchers, innovators, dreamers, and doers, who are creating a technology with the potential to transform the way we move. - We’re creating first-of-its-kind technology that will transform transportation. - To do so successfully, we must design for everyone in our cities and on our roads. - We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. - Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do. - Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. - We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges. - Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. - Headquartered in Boston, Motional has operations in the U.S and Asia.
The all-in-one jobsite management software for field to office communication.
• Design, build, and maintain automated data pipelines that move data from source systems (Salesforce, Xero, Ramp, product databases) into our central data lake and warehouses • Own the end-to-end data architecture, including storage strategy, processing systems, and pipeline orchestration • Implement and maintain ETL/ELT workflows that extract, transform, and load data into clean, analytics-ready formats • Partner with Data Insights Managers and business stakeholders to translate reporting requirements into robust technical data solutions • Build automated validation and quality-check layers into every pipeline to prevent bad data from reaching reporting layers • Monitor pipeline health in real time; triage and resolve failures quickly to meet data availability SLAs • Enforce data standards, naming conventions, schema consistency, and access controls across all systems • Support integration and maintenance of key tools including Salesforce, Xero, Ramp and Greenhouse into the data lake • Maintain auditability of all data flows and support compliance and governance requirements • Collaborate with the DIM TL and Director of Operations on the data roadmap and architectural decisions.
Hungryroot is the online grocery service that makes healthy eating easy and personal.
• Develop pipelines in Spark (Python) in the Databricks Platform • Build cross-functional working relationships with business partners in Food Analytics, Operations, Marketing, and Web/App Development teams to power pipeline development for the business • Ensure system reliability and performance • Deploy and maintain data pipelines in production • Set an example of code quality, data quality, and best practices • Work with Analysts and Data Engineers to enable high quality self-service analytics for all of Hungryroot • Investigate datasets to answer business questions, ensuring data quality and business assumptions are understood before deploying a pipeline
Role Description We are looking for a freelance Data Engineer focused on data integration to build and maintain the pipelines that connect our many source systems into a unified, trustworthy data foundation. You will design ETL/ELT processes, integrate APIs and third-party platforms, model data for reporting, and ensure data quality across the organization. This is a fully remote role working closely with analytics, finance, and engineering stakeholders. Key Responsibilities - Design, build, and maintain scalable ETL/ELT pipelines that move and transform data between systems. - Integrate data from diverse sources — APIs, databases, SaaS platforms, flat files, and spreadsheets — into a central warehouse. - Develop and maintain connectors and reconciliation logic across business systems (e.g., project management, time-tracking, finance, and invoicing tools). - Model and structure data for analytics, reporting, and downstream applications. - Implement data validation, quality checks, monitoring, and alerting to ensure accuracy and reliability. - Optimize queries, storage, and pipeline performance for cost and speed. - Document data flows, schemas, mappings, and transformation logic. - Collaborate with analysts, finance, and engineering teams to understand requirements and deliver clean, usable datasets. - Support data governance, security, and privacy best practices. Qualifications - 3+ years of experience in data engineering, data integration, or a closely related role. - Strong SQL skills and experience with relational databases (PostgreSQL, MySQL, SQL Server). - Proficiency in Python (or a comparable language) for data processing and automation. - Hands-on experience building ETL/ELT workflows and orchestration (e.g., n8n, or similar). - Experience integrating REST APIs, webhooks, and third-party SaaS data sources. - Experience with data warehouses (BigQuery, Snowflake, or similar). - Understanding of data modeling, warehousing concepts, and data quality practices. - Comfort working with messy, real-world data across formats (CSV, Excel, JSON, XML). - Strong problem-solving skills and clear communication in a remote team. Nice to Have - Experience with cloud data platforms (AWS, or Azure). - Exposure to finance or operational data and cross-system reconciliation. - Experience with BI/visualization tools (Looker, Power BI, Tableau). - Experience in an agency or multi-entity, multi-currency environment. Benefits - Fully remote working with flexible hours. - A collaborative team spanning engineering, analytics, and finance. - Competitive salary and benefits package. How to Apply Submit your CV along with examples of pipelines, integrations, or data projects you have delivered.
NAVANTA is the community bank technology outfitter that inspires confidence for community banks, by providing purpose-built solutions that make technology work for them, instead of the other way around. Founded in 1991, our purpose is to Empower Community Banks and Our People to Thrive – Together. We live that Purpose by always putting people first in our decisions and actions. Our engaged culture is strongly influenced by the passion our team members bring while serving Community Banks and their communities. We believe in encouraging confidence in each other and delivering solutions that make our customers confident with us. To that end we seek out problem solvers, creative thinkers and engaged individuals that thrive in a fast-paced yet supportive environment. We believe engaged employees lead to loyal customers, which in turn drives results for our business. We are caring, intense, and approachable, and have a lot of fun along the way.
Role Description The Lead Data Engineer owns the Navanta data backbone — public Call Report data in the early build, and secure ingestion from bank cores into lakehouses as each client’s on-premises environment is stood up. Working under the SVP of Technology and Commercial AI and in close partnership with the AI/ML, security, and platform teams, this role builds the architecturally clean, well-modeled, reconcilable data foundation that makes it possible for the Navanta AI platforms to give numbers a banker will act on. Key Responsibilities - Design the lakehouse: Apache Iceberg (or similar technology) on object storage, a catalog for table management and per-bank isolation, dbt models, and a query engine - Build secure, least-privilege ingestion from bank systems — log-based CDC where permitted, with query-based and batch/SFTP fallbacks, plus an in-bank collector pattern - Own data modeling for the semantic and metric layer (deposits, concentration, uninsured exposure, asset quality, and peer groups) - Handle schema drift, data quality, and reconciliation; make ingestion observable and recoverable - Partner with the AI/ML team on the structured-query path and with Security on PII classification at landing, in alignment with regulatory data-handling requirements - Document data lineage, transformation logic, and access controls to support audit and exam readiness - Define and enforce data contracts, quality thresholds, and alerting for pipeline failures Core Competencies - End-to-end ownership of ingestion-through-serving pipelines, with a bias toward reliability and observability - Rigorous data modeling for analytics — semantic layers, metric definitions, and reconcilable outputs - Security and compliance mindset: PII handling, least-privilege access, and data governance aligned to regulatory guidance - Cross-functional partnership with AI/ML and platform engineering to deliver governed, queryable data products Key Performance Indicators (KPIs) - Data freshness and pipeline reliability — SLAs met for data ingestion and bank-core feeds - Data quality score across key metrics versus source reconciliation - Time to onboard a new bank’s data environment, from kickoff to queryable lakehouse - PII classification coverage at landing and zero unauthorized data-access incidents - Semantic layer adoption — percentage of assistant queries resolved via governed metrics versus ad hoc SQL Qualifications - 8–12+ years in data engineering with end-to-end ownership of ingestion through serving, and 2+ years in a lead or senior role - Strong Python and expert SQL; rigorous data modeling for analytics - Hands-on lakehouse experience (Iceberg/Delta/Hudi or equivalent) and modern transformation tooling - Built reliable pipelines from messy operational and transactional source systems - Comfort with CDC mechanics and the realities of pulling from databases you do not control Core Technologies - Languages: Python, SQL (deep) - Lakehouse & catalog: Apache Iceberg; Polaris / Nessie / Lakekeeper - Transform & query: dbt; Trino / Presto / DuckDB - CDC & streaming: Debezium (SQL Server CDC, Postgres logical replication), Kafka / Redpanda - Orchestration: Dagster (or Airflow) - Storage: S3 / MinIO - SQL Server and PostgreSQL data modeling, pgvector (or equivalent) Nice to Have - Experience with financial or core-banking data, or FFIEC / Call Report data specifically - Strong SQL Server familiarity - Data contracts, lineage, and governance practices Education and/or Experience - Bachelor’s degree in computer science, mathematics, information systems, or a related field, or equivalent hands-on experience - Experience in the financial services industry or a regulated data environment strongly preferred Work Structure & Expectations - Full-time role combining ongoing pipeline operations with initiative-based lakehouse build-out and new bank onboarding - Close collaboration with AI/ML, platform engineering, and security teams; on-call rotation covering data pipeline reliability Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. - While performing the duties of this job, the employee is regularly required to sit and use hands to finger, handle, or touch objects, tools, or controls. - The employee frequently is required to talk or hear. - The employee is occasionally required to stand; walk; and stoop, kneel, crouch, or crawl. - The employee must occasionally lift and/or move up to 10 pounds, usually waist high, up to 50 feet away. - Specific vision abilities required by this job include close vision and the ability to adjust focus. Work Environment - Typical office environment - Up to 20% travel time may be required Company Description Navanta is the trusted technology and services partner for community financial institutions, unifying critical systems, security, cloud infrastructure, and support into one seamless, purpose built experience. With more than 35 years of banking expertise — from Managed IT to Core Banking, CRM, and Advisory Services — Navanta helps institutions simplify complexity, reduce risk, and strengthen daily operations. Navanta empowers community bankers and their people to thrive together. Go Bankers, Go.™
Role Description The Data Platform and Data Enrichment teams are in charge of making our unified database accessible, usable, enrichable and reliable for all our teams internally. The team owns the Lakehouse that powers Launchmetrics' data products — a Databricks-based platform built on Delta Lake, S3, and following a Bronze/Silver/Gold medallion architecture. This layer stores, enriches, and serves the media intelligence data behind Discover (our client-facing platform), Genie (internal tooling), and Delta Sharing. You'll work on a batch-first system that simulates real-time behavior using primitives like Change Data Feed, S3 event triggers, serverless compute, and liquid clustering. This role plays a key part in our Tech & Product strategy, directly supporting company-wide objectives around customer retention, data trust, and the development of new AI-based insights. What you'll do - Design and build data pipelines (batch and near-real-time) using PySpark and Databricks, across the Bronze/Silver/Gold medallion layers - Architect efficient Delta Lake table schemas — partitioning/liquid clustering strategy, schema evolution handling, and enrichment workflows - Work closely with product, QA, and other data engineers to translate enrichment and search requirements into reliable pipelines - Own code quality: structured PySpark jobs, unit tests (pytest), and adherence to team conventions - Continuously improve pipeline reliability and cost efficiency (OPTIMIZE scheduling, retry/backoff logic, concurrency handling) - Participate in cross-pod initiatives across the data platform Technical Stack - Languages: Python, PySpark, Typescript - Data Platform: Databricks, Delta Lake, Delta Sharing - Storage: AWS S3, MySQL - Cloud: AWS (S3, Kinesis, Lambda, SQS, ECS, Step Functions, …) - Tools: Jira, Databricks Asset Bundles, Serverless, Claude Code - Versioning: Git, GitHub - CI/CD: GitHub Actions - Testing: pytest, Jest Qualifications - Engineer Degree or Master Degree in Computer Science and 3+ years of relevant work experience in full-stack development in a SaaS environment - Strong Python and PySpark experience; comfort with distributed data processing at scale - Experience with a Lakehouse architecture (Databricks, Delta Lake, or comparable — e.g. Spark on EMR/Glue) - Familiarity with medallion architecture patterns (Bronze/Silver/Gold) or similar layered data design - Ability to reason about schema evolution, partitioning/clustering strategy, and pipeline reliability (retries, idempotency) - Ability to traverse logical sequences of either procedural or object-oriented code, abstracted or static - and understand it entirely - Bright, energetic, highly motivated self-starter with experience in a fast-paced, results-oriented organization - Ability to adapt, estimate workload, break down a task into logical steps, solve problems, self-improve and suggest new ways of improvement - Last, but definitely not least: you speak, read, and write English fluently How we hire - Intro call with Talent Acquisition, to get to know each other (30 min). - Meet & Greet with VP Software Development. - Technical Interview, a live interview with a couple of Software Engineers. - Team fit with the engineers and peers you'd build alongside. Benefits - Learning and development allowance - Benefits package tailored to your location - Flexible working arrangements with support to set up your home office - Room to grow along whichever path fits you - Remote-friendly, with hubs across our twelve markets OUR COMMITMENT Launchmetrics is proud to be an Equal Opportunity Employer building a diverse and inclusive workforce. If there is anything extra we can do to help you feel at ease during your interview process, please let the PeopleOps team member you’ll be meeting with know.
• Own the reliability, availability, and accuracy of our data infrastructure • Build, maintain, and improve our data pipeline using our modern data stack • Build centralized, durable, and reusable data models • Build and maintain a semantic layer with canonical dimension and metric definitions • Partner closely with Analysts, PMs, Engineers, Marketing, Legal, Fraud, and other stakeholders • Build Reverse ETL pipelines in Python • Proactively bring in new data sources • Champion data governance and help elevate the organization's data maturity
• Design and build full-stack features spanning React frontends, Python/FastAPI backends, and supporting data models and transformations • Develop and maintain data models and transformation pipelines (dbt preferred) that feed application and analytics layers; ensure data flowing into applications is well-modeled, tested, and reliable • Design and implement REST APIs serving clinical data, quality metrics, care gaps, and decision support content to internal applications and external partners • Implement API contracts, versioning strategies, authentication/authorization patterns (OAuth/OIDC), and rate limiting for compliant clinical data access • Build responsive, accessible React user interfaces with modern component patterns; collaborate with product and clinical teams to translate requirements into intuitive UIs • Design and implement comprehensive testing strategies — unit tests, integration tests, end-to-end tests, and data validation tests — to ensure reliability across the stack • Conduct and support QA activities including test planning, test case design, manual testing, and establishing testing standards; work closely with QA engineers and clinical testers to validate functionality and user experience • Write clean, tested, maintainable code across the stack; participate actively in code review and help raise code quality standards • Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, or similar) deliberately and effectively — leveraging them for scaffolding, refactoring, test generation, and documentation while maintaining code quality and understanding • Define and measure success metrics for features — including usage, adoption, clinical workflow impact, and data quality — to drive iterative improvements and prioritization • Partner with product and data teams to establish KPIs and dashboards that measure feature impact on clinician workflows, care coordination, and operational efficiency • Troubleshoot and resolve issues across the full stack — from UI bugs to API failures to data pipeline problems; trace issues end-to-end and implement durable fixes • Collaborate with data engineering to ensure API data contracts are well-defined and upstream data models support application needs • Participate in architecture and design discussions including API design, authentication patterns, data contract definition, and system reliability • Raise data quality or data modeling concerns early in the development process rather than letting them surface downstream • Contribute to technical documentation including API specifications, data dictionaries, runbooks, and architectural decisions • Support production systems through on-call rotations, incident response, and post-incident improvements • Mentor junior engineers and contribute to team process improvement and knowledge sharing
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Python, SQL, AI, AI/ML, Observability/Monitoring, Airflow
