• Assist in developing and maintaining basic data ingestion and transformation pipelines (ETL/ELT) using PySpark and SQL. • Help monitor data pipelines and implement basic checks to ensure data reliability for internal consumers (such as Data Scientists). • Learn and assist in automating pipeline testing and deployment processes. • Work alongside data scientists and software engineers to understand and support integrated data flows. • Assist in documenting data schemas, pipeline architectures, and metadata cataloging.

• Support Army logistics enterprise data management through data governance, data quality, metadata management, data modeling, and strategic initiatives. • Conduct research and analysis to evaluate enterprise data, business processes, data quality, and emerging issues affecting Army logistics operations. • Develop and maintain enterprise data models, business rules, metadata, and documentation supporting Army logistics information requirements. • Support data governance activities by implementing standards, policies, procedures, and best practices that improve enterprise data management. • Analyze, validate, and monitor data quality to identify inconsistencies, recommend corrective actions, and improve data integrity across enterprise systems. • Collect, consolidate, validate, and analyze information from multiple enterprise data sources to support executive decision making and organizational priorities. • Develop executive-ready briefings, reports, decision papers, presentations, dashboards, and analytical products supporting Army logistics leadership. • Coordinate activities and recommendations across multiple Army organizations and stakeholders to improve enterprise data management and governance. • Facilitate meetings, working groups, and collaborative planning sessions while documenting decisions and tracking follow-up actions. • Support enterprise data stewardship, information management, reporting, and modernization initiatives that improve organizational performance. • Develop performance metrics, dashboards, and analytical products to support leadership visibility and informed decision making. • Support business development activities, including market research, proposal support, solution development, and client engagement, as required. • Perform additional consulting, analytical, and project support activities as assigned.

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.