Sr Specialist, Data Engineering

Design, build, and maintain production-grade data pipelines in Airflow that ingest data from digital touchpoints into Snowflake.

Develop modular, tested, and well-documented dbt models that transform raw data into reliable, business-ready datasets — owning the full lifecycle from source definition to exposure.

Provision and manage cloud data infrastructure (Snowflake objects, Airflow environments, supporting GCP resources) through Terraform, with everything version-controlled and peer-reviewed.

Implement and uphold data quality, observability, and testing standards across pipelines

Tune Snowflake performance and manage warehouse cost — clustering, query profiling, resource monitors and treat cost as a first-class engineering concern.

Operate the on-call and incident response cycle for owned pipelines: triage failures, perform root-cause analysis, write post-mortems, and convert recurring issues into permanent fixes.

Implement pipelines to platform standards — branching strategy, CI/CD for dbt and Airflow, code review norms, documentation, naming conventions 

• Stay current on the evolving data engineering stack (agentic tooling, streaming patterns, observability frameworks) and bring grounded recommendations on what to adopt and what to skip.