Senior Data Engineer

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The Senior Data Engineer is the technical anchor for the Strategic Partnerships team's data platform. They own the design and evolution of ETL pipelines, semantic models, and the underlying data infrastructure that powers cross-functional initiatives with Marketing, Finance, Sales, Legal, HR and Product. Because the team operates on a project/intake basis across many business units, this role requires someone who can move between domains quickly — ingesting unfamiliar data, modeling it correctly the first time, and shipping pipelines that stakeholders can rely on without hand-holding.

This is not a pure pipeline-building role. The Senior Data Engineer sets the standards the rest of the team follows — how we model data in dbt, how we orchestrate in Airflow, when to reach for Fivetran vs. a custom DAG, how we handle PII across BUs, and how we keep infrastructure reproducible via Terraform. They partner directly with senior stakeholders across business units, translating ambiguous asks into durable data products, and they mentor the rest of the team on technical execution.

What Already Exists:

The Senior Data Engineer will work within an established data stack that includes:

• Warehousing: Snowflake as the primary warehouse; BigQuery for GCP-native workloads
• Ingestion: Fivetran for SaaS-to-Snowflake pulls; Airflow (on AWS) for custom DAGs, Lambda-based extraction, and S3 staging
• Modeling: dbt for transformation and semantic layer definition
• Custom compute: Databricks for bespoke modeling work that doesn't fit cleanly into dbt/Snowflake
• Infrastructure: Terraform for managing Fivetran connections, GCP infrastructure, and AWS resources
• Version control & CI/CD: GitHub
• Consumption: Tableau dashboard; internal tools built on top of the semantic layer

What You'll Do: 

• Design and own end-to-end data pipelines across Fivetran, Airflow, dbt, and Databricks — selecting the right tool for each problem rather than defaulting to one
• Architect and maintain the team's semantic layer in dbt, ensuring models are reusable, well-tested, and documented for downstream analysts, tools, and BU stakeholders
• Set and enforce engineering standards across the team — testing, CI/CD, code review, observability, data quality, and documentation
• Partner directly with senior stakeholders across Marketing, Finance, Sales, Legal, HR and Product to scope data needs, advise on tradeoffs, and translate ambiguous business problems into durable data solutions
• Own Terraform-managed infrastructure for the team's Fivetran connectors, GCP resources, and AWS components (Airflow, S3, Lambda)
• Drive architectural decisions on ingestion patterns, warehouse design, and streaming vs. batch tradeoffs
• Establish data governance practices for the team — PII handling, access controls, lineage, and compliance with Finance and Legal data requirements
• Mentor teammates on technical execution, code quality, and system design
• Monitor pipeline SLAs, proactively identify failure modes, and lead incident response for data issues affecting partner BUs

What You Bring: 

• 6+ years of professional experience as a Data Engineer, with demonstrated ownership of production pipelines at scale
• Expert-level SQL and Python; comfortable optimizing complex queries, profiling pipeline performance, and writing production-grade code
• Deep experience with a cloud data warehouse (Snowflake strongly preferred; BigQuery or equivalent acceptable)
• Strong dbt experience — has designed semantic layers, established modeling conventions, and led dbt adoption or maturation on a team
• Hands-on experience with orchestration tools — Airflow, Fivetran required; Dagster or equivalent a plus
• Experience with Databricks and Spark for custom modeling and transformation workloads
• Experience with AWS (Airflow, S3, Lambda) and GCP (BigQuery and adjacent services); comfortable operating across both clouds
• Demonstrated ability to influence architectural decisions and set standards that other engineers follow
• Strong stakeholder management — can run scoping conversations with senior non-technical partners and translate business needs into technical specs
• Security-first mindset; experience with PII handling, access controls, and data governance in regulated environments (SOX, GDPR, or similar)
• Has built something end-to-end before specializing — values broad competence paired with depth

Skills

Core: Python, SQL, Snowflake, dbt, Airflow, Fivetran, Databricks, AWS (S3, Lambda, Airflow), GCP (BigQuery), GitHub, Terraform

Required: Production experience with at least one streaming technology (Kafka preferred); experience designing semantic layers; experience with CI/CD for data pipelines

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