Asks for

The ad says“Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC”
The ad says“Quality, lineage, and governance: Ensure the team implements automated testing, anomaly detection, validation, lineage/metadata, and documentation as a standard part of delivery”
The ad says“Quality, lineage, and governance: Ensure the team implements automated testing, anomaly detection, validation, lineage/metadata, and documentation as a standard part of delivery”
The ad says“Experience running workloads in AWS (or similar cloud): storage, compute, networking basics, cost controls”
The ad says“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”
The ad says“Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC”
The ad says“Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns”
The ad says“Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC”
The ad says“Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC”
The ad says“Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners”
The ad says“Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners”
The ad says“Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners”
The ad says“Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns”
The ad says“Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns”
The ad says“Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns”
The ad says“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”
The ad says“Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners”

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

Benefits

Culture and benefits built around work-life balance
Extensive resources to support physical and mental health
Employee assistance program
Generous retirement plans
Incentives for referring new colleagues
Learning, training and mentoring programs
Cross-functional projects
Stretch assignments to help you progress
Receive feedback and earn bonuses from peers & managers
Employee bonding activities
ESG and charitable team initiatives

Preferred experience

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
Strong data modeling skills, and the judgment to set standards other engineers will follow
Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns
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
Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners
Data observability platforms; data catalog/lineage tooling; data access governance
Experience shaping product-facing datasets and semantic layers (e.g., for BI and APIs)
Prior experience as an EM or tech lead managing a team of 3+ data or software engineers