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Role Description The Technical Architect - Enterprise Data is responsible for defining, governing, and delivering scalable enterprise data and integration architectures across Microsoft Fabric, Azure, and core business systems. This role provides architectural leadership, ensures alignment with enterprise standards, and guides engineering teams in implementing high-quality, secure, and performant solutions. The architect partners with business stakeholders, data teams, systems and security teams, and application owners to translate requirements into technical & functional blueprints and long-term platform roadmaps. The role will report to the IT Applications team and will work with key IT stakeholders. Role Responsibilities: - Define Enterprise Data Architecture across Fabric, Data Lake, EDW, and analytics platforms. - Design Integration Patterns for ingestion, transformation, and consumption. - Create architectural flows and reference models. - Establish standards for data modeling, metadata, lineage, and governance. - Implement data validation, logging, monitoring, lineage, and RBAC. - Ensure compliance with enterprise security, confidentiality, and regulatory standards. - Drive platform cost optimization, performance tuning, and operational excellence. - Enforce architectural governance and adherence to standards. - Collaborate with business stakeholders and source system teams to understand data requirements. - Translate business needs into technical specifications and architectural roadmaps. - Review user stories and provide architectural guidance to development teams. - Support QA and functional teams in defining testing strategies for new and existing features. - Ensure delivered solutions align with business needs and architectural intent. - Integrate AI/ML Models and GenAI capabilities into data products and workflows. - Evaluate emerging technologies and recommend adoption strategies. - Guide teams on responsible AI usage and integration patterns. - Conduct root cause analysis for data issues and identify opportunities for improvement. - Manage platform reliability, data quality, and governance processes. - Define the continuity strategy for all data and integration platforms. - Proactively identify architectural risks (single points of failure, dependency bottlenecks, and data corruption risks). - Ensure every solution includes DR and continuity considerations from day one. - Design continuity across ERP, CRM, Data Lake, Fabric, and analytics systems. - Ensure teams know how to recover systems, where documentation lives, and how to execute continuity plans. - Ensure continuity plans meet audit and security requirements. - Set Vendor Expectations. - Define clear standards for process adherence, development methodology, documentation, and delivery timelines for all vendor-led work. - Drive Quality Accountability. - Establish quality gates, review cycles, and acceptance criteria to ensure vendor deliverables meet architectural and coding standards. - Conduct regular checkpoints with vendor teams to monitor progress, identify risks, and ensure alignment with enterprise architecture principles. - Provide technical guidance to vendor engineers to ensure solutions are scalable, secure, and compliant with enterprise patterns. Qualifications - 10+ years in enterprise solution architecture, data engineering, and software development. - Degree preferably in Computer Science, Information Technology, Management Information Systems, or Accounting. - Proven experience designing end-to-end integrations, data platforms, and automation for large enterprise environments. - Strong hands-on expertise with Microsoft Fabric, Data Lake, Data Warehouse, Data Pipelines, and the broader Microsoft ecosystem. - Deep experience with Power BI semantic models, datasets, dashboards, and reporting. - Advanced proficiency in DAX, Power Query, SQL, Python, and PySpark. - Demonstrated experience architecting ETL/ELT pipelines integrating diverse data sources into Azure Data Lake and EDW. - Strong understanding of data modeling, normalization, metadata management, and enterprise data security. - Exposure to AI/ML, Copilot, GenAI, LLMs, prompt engineering, and AI API integration. - Familiarity with SDLC, software quality practices, Jira, Git, CI/CD. - Experience with SaaS/Cloud ERP or CRM systems (NetSuite, Salesforce, SAP S/4HANA). - Excellent communication, stakeholder management, and problem-solving skills. Additional Information Arista Networks is an equal opportunity employer. Arista makes all hiring and employment-related decisions in a non-discriminatory manner without regard to race, color, religion, sex, sexual orientation, gender identity, national origin or any other factor determined to be unlawful under applicable federal, state, or local law. All your information will be kept confidential according to EEO guidelines.
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• 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.™
• 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
• Handle support tickets and operational issues reported by internal teams and external partners; investigate root causes and coordinate resolution with senior engineers • Perform KTLO (Keep The Lights On) tasks including monitoring pipeline health, responding to alerts, validating data quality, and investigating data anomalies • Conduct data source discovery and profiling work — examining raw data sources, documenting data structure, identifying quality issues, and recommending integration approaches • Assist with data validation and testing — writing SQL queries to validate data transformations, identifying gaps and inconsistencies, and flagging issues for review • Support data quality initiatives by running diagnostics, documenting data quality findings, and escalating issues with clear context for senior engineers • Assist in establishing and monitoring data quality metrics — working with senior engineers to define quality KPIs and track pipeline health • Help maintain and improve documentation for existing data systems, pipelines, and data sources — documenting schemas, transformation logic, and known issues • Assist senior engineers with debugging data pipeline issues — tracing data through transformations, validating intermediate outputs, and comparing expected vs. actual results • Conduct quality assurance activities — reviewing data outputs, testing transformations, and validating correctness before data reaches downstream consumers • Perform exploratory data analysis to understand data patterns, support analytics requests, and help answer business questions about data availability and quality • Learn and apply data engineering best practices including version control (Git), code review processes, and testing frameworks under guidance from senior engineers • Support infrastructure and operational tasks as assigned — assisting with deployments, maintaining environments, and supporting on-call activities • Participate in knowledge-sharing and mentorship; ask questions, document learnings, and contribute to team documentation and runbooks
Role Description The AI/ML Engineer builds, deploys, and supports production forecasting and AI capabilities on top of the company’s governed Databricks lakehouse foundation. This role focuses on: - Feature engineering - Training and inference workflows - Model evaluation - Model serving - Monitoring - AI-ready outputs that support forecasting, business automation, intelligent recommendations, executive reporting, and AI/BI consumption This role is accountable for turning trusted enterprise data into production AI and forecasting capabilities that are reliable, measurable, governed, and usable by the business. Responsibilities include: - Build and maintain feature pipelines, training datasets, and forecast workflows for various use cases including revenue, demand, delivery timing, customer behavior, inventory risk, process performance, and operational planning. - Operationalize forecasting and machine learning models through repeatable training, evaluation, deployment, inference, and monitoring patterns in Databricks. - Deploy and support batch, near-real-time, and API-based inference outputs for dashboards, Databricks Apps, workflow automation, business alerts, and decision-support tools. - Implement model performance tracking, drift monitoring, validation checks, error handling, and traceability from source data through feature logic to prediction output. - Partner with Data Engineering and BI teams to ensure forecast outputs, KPIs, business logic, and AI-enabled metrics align to governed semantic structures and reporting standards. - Create reusable notebooks, libraries, feature engineering patterns, evaluation templates, and deployment frameworks that accelerate enterprise AI adoption while remaining supportable. - Support AI/BI and agent-based consumption by preparing structured, governed, business-readable outputs that can be used by reporting tools, applications, and AI assistants. - Translate forecasting and AI outputs into measurable operational or financial impact, including revenue opportunity, margin improvement, demand planning, service performance, inventory optimization, and process automation. Qualifications - 5+ years of experience in machine learning engineering, data engineering, analytics engineering, applied AI engineering, or production forecasting. - Strong hands-on experience with Databricks, Spark, SQL, Python, and production-grade data pipeline development. - Experience building forecasting or machine learning solutions in production, including feature preparation, model training, evaluation, deployment, monitoring, and support. - Experience with model lifecycle practices, versioning, validation, performance tracking, and production release processes. - Ability to connect technical AI and forecasting work to measurable financial, operational, or customer-facing outcomes. - Strong understanding of data quality, metric consistency, semantic validation, and governed enterprise reporting needs. Requirements - Experience with MLflow, Mosaic AI Model Serving, Unity Catalog-governed AI assets, Databricks Workflows, Feature Engineering, and model monitoring patterns. - Experience supporting forecasting, automation, or recommendation use cases in manufacturing, distribution, supply chain, finance, sales, or service operations. - Experience preparing AI-ready outputs for dashboards, applications, workflow automation, or conversational AI/BI use cases. - Exposure to vector search, retrieval-augmented generation, agentic workflows, or AI assistant patterns is a plus. - Experience with time-series forecasting, demand planning, revenue forecasting, inventory optimization, or operational prediction models is strongly preferred. Benefits - 100% employer paid medical plan - 401(k) match - Additional medical plans - Dental and vision coverage - Flex spending account - Short-term and long-term disability & life insurance coverage
Open | Cloud-Native | Purpose-Built for Science
Role Description TetraScience is building the scientific data and AI cloud for biopharma. The platform is the innermost foundation for developing, delivering and operating our enterprise grade, secure, compliant Scientific Data and AI capabilities customers rely on. In this role, you will own the platform architecture, evolution and growth scaling across: - Enterprise Platform - Scientific Search - AI/ML Ops - Developer Platform - Developer Productivity - Lakehouse Platform - Partner Integrations - Cloud Infrastructure This is a senior IC leadership role. You set technical direction, own the decisions that cross team boundaries, and close architectural gaps before they become business risks. After achieving strong product-market fit and traction, we are entering a growth scaling phase where we are expanding our industry partnerships and developer experience to rapidly build the foundations of AI-native scientific data and workflows in production. The scope of this role is intentionally broad. We are looking for experienced candidates who cover a majority of these areas. Strong candidates bring deep fingerprints in one of two architectural profiles, with meaningful range across both: - Enterprise Data & AI Platforms - Data, Knowledge, and Developer Products Qualifications - 12+ years in software engineering, with at least 5 at staff or principal level in a SaaS platform or data infrastructure context. - Deep architecture ownership in at least one of the two fingerprint profiles above, with meaningful range across the other. - Demonstrated ownership of enterprise authentication and authorization systems at scale: SAML, OIDC, fine-grained RBAC across a multi-tenant SaaS product. - Hands-on experience with AI/ML serving infrastructure: you have built and operated model inference pipelines under production load. - Search architecture experience: you have designed and operated a search platform that handles diverse query types (keyword, semantic, or hybrid) across large structured or semi-structured datasets. - Hands-on experience with data lake architectures at scale: Delta Lake or Apache Iceberg, schema evolution patterns, partition pruning, and the trade-offs between query performance and storage cost. - Infrastructure fluency on AWS with Kubernetes or ECS. - Ability to write and defend architecture decisions: RFCs, trade-off documents, design reviews. - Strong cross-team communication. - Comfort operating across strategy, architecture, and operations in the same week. Requirements - Authn/Authz architecture is documented, consistent across services, and passing enterprise security reviews without heroics from a single engineer. - AI/ML infrastructure has a clear architecture and roadmap for MLE inference and training use cases, with strong operational telemetry and cost visibility. - The developer platform has clear SDKs and a set of standard templates for scientific use cases to start from, with adoption and delivery by multiple scientific use case teams. - Operational excellence based on a clear O11y architecture rolled out, with every production service having SLOs defined, monitored and managed. - Cost governance with customer chargeback attribution architecture and operationalized with the finance and field teams. - Lakehouse platform architecture and operational buildout as a Data Products Platform with strong DX and operational scaling. - Evolve IDS to open standards based schema and encoding with strongly typed data models and schema-on-write enforcement. - Published reference architecture for each partner class (lab instrument manufacturers and AI models), with one partner successfully onboarded against each without bespoke engineering support. Benefits - Competitive compensation with equity - Unlimited PTO - Company-paid Life Insurance, LTD/STD - 401(k)
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Python, SQL, ETL, Observability/Monitoring, AI/ML, Data Engineering
