Data Engineering Team Lead, Datasite

• 6+ years building and operating production data systems at scale, including prior experience leading a team or mentoring senior engineers
• A track record of managing people or leading a team through ambiguous, high-stakes initiatives to clear results — including hiring, performance management, and career development
• Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns
• Strong data modeling skills, and the judgment to set standards other engineers will follow
• Experience running workloads in AWS (or similar cloud): storage, compute, networking basics, cost controls
• Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC
• Comfort operating as both an individual technical contributor and a people manager — able to context-switch between deep technical work and team leadership day to day

Desirable

• Grata is the leading private-market dealmaking platform—bringing the most comprehensive, accurate, and searchable proprietary data on private companies, financials, and owners to investors, advisers, and corporate deal teams. With 700+ customers and recognition from G2 and PE Wire, we're growing fast and shaping the future of data-driven dealmaking
• We're looking for a Data Engineering Team Lead to own the technical direction and delivery of the team that builds and operates the data platforms powering Grata's products and analytics
• You'll split your time between hands-on technical leadership — architecture, design review, and unblocking hard problems — and people leadership: growing engineers, setting priorities, and making sure the team ships reliable, high-leverage work
• You'll partner closely with Product, Data Science, and Application Engineering, and represent the data platform in cross-functional planning
• Lead the team: Manage and grow a team of Data Engineers — set goals, run 1:1s, give feedback, and own hiring and performance for the team
• Own technical direction: Set architecture and design standards for ELT/ETL pipelines in Python/SQL/Spark on Databricks, and make the build-vs-buy and platform trade-off calls for the team
• Evolve the lakehouse & warehouse: Drive dimensional modeling/star schemas and incremental patterns (e.g., Delta Lake/CDC), balancing cost, performance, and usability across the team's roadmap
• Streaming & event data: Set the technical bar for real-time/near-real-time jobs, idempotency, and exactly-once semantics; review designs before they ship
• Quality, lineage, and governance: Ensure the team implements automated testing, anomaly detection, validation, lineage/metadata, and documentation as a standard part of delivery
• Scale & reliability: Own SLOs and on-call rotations for data platforms; make sure monitoring, alerting, and capacity/cost management scale with the business
• Prioritize & sequence delivery: Break down ambiguous initiatives into a roadmap, sequence the team's work, and hold the team accountable to measurable improvements in freshness, completeness, and query performance
• Partner across functions: Represent the data platform with Product and DS on source selection, feature readiness, and experiment design; with App Eng on stable APIs and semantic layers
• Raise the bar: Lead design and code review, run brown-bag sessions, and coach engineers toward greater technical ownership and independence