Data Engineer III

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The Data Engineer III is a core builder on the Strategic Partnerships team's data platform. They design and ship ETL pipelines, semantic models, and the supporting 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 context-switch between domains, get up to speed on unfamiliar data quickly, and deliver pipelines that stakeholders can rely on without hand-holding.

The Data Engineer III operates with a high degree of autonomy. They own projects end-to-end — from scoping with BU stakeholders, to building and deploying pipelines, to monitoring them in production. They follow the standards set by the team and contribute back to them as they grow in the role.

What You'll Do:
The Data Engineer III will work within an established data stack that includes:

Warehousing :
Snowflake as the primary warehouse;
Big Query 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 :
Git Hub

Consumption :
Tableau dashboards; internal tools built on top of semantic data layers

Responsibilities:

Build and own end-to-end data pipelines across Fivetran, Airflow, dbt, and Databricks — selecting the right tool for each problem with guidance from the Senior Data Engineer on novel cases

Design and maintain dbt models that feed the team's semantic layer, ensuring they are tested, documented, and reusable

Partner directly with stakeholders across Marketing, Finance, Sales, Legal, HR, and Product to scope data requirements and translate them into pipeline specs

Contribute to Terraform-managed infrastructure for Fivetran connectors, GCP resources, and AWS components

Follow and contribute to team engineering standards — testing, CI/CD, code review, observability, and documentation

Independently resolve complex ETL and data quality issues, escalating only when architectural tradeoffs are in play

Monitor pipelines for SLA compliance and participate in incident response

Apply data governance practices to ensure PII is handled correctly and BU-specific compliance requirements (Finance, Legal, HR) are met

Communicate technical concepts clearly to non-technical stakeholders and advise them on what is and isn't feasible

What You Bring:

4+ years of professional experience as a Data Engineer, with demonstrated ownership of production pipelines

Strong SQL and Python; comfortable writing production-grade code, debugging complex pipelines, and optimizing queries

Hands-on experience with a cloud data warehouse (Snowflake strongly preferred; Big Query or equivalent acceptable)

Solid dbt experience — has built and maintained models in a production dbt project

Experience with orchestration tools — Airflow and 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 (Big Query and adjacent services)

Ability to work autonomously — can take an ambiguous…