Sr. Data Engineer

Overview We are hiring a Senior Data Engineer to own and evolve our analytics data platform. The infrastructure is already in place (PostgreSQL, Airflow, Jupyter, SQLMesh). The focus of this role is not server setup or hardware maintenance, but building robust pipelines, high‑quality data models, and scalable transformation workflows. You will work closely with three data analysts and partner directly with departments like Marketing or Sales to ensure reliable, well‑structured datasets power reporting and analysis in Sigma. Responsibilities Data Pipeline Development Build and maintain ingestion pipelines from multiple sources, including: Aircall, HubSpot, Internal proprietary applications Implement reliable, testable transformation workflows using SQL Mesh Orchestrate and monitor jobs in Airflow Ensure incremental, performance, and maintainable data pipelines Data Modeling & Transformation Design analytics‑ready data models in PostgreSQL Develop clean, well‑documented dimensional models Optimize queries and warehouse performance Establish transformation best practices and review standards Marketing Data Enablement Work closely with Marketing stakeholders to understand attribution, funnel, campaign, and performance requirements Translate business requirements into scalable data models Prepare curated datasets for Marketing analysts Required Qualifications 5+ years of experience in data engineering Strong expertise in PostgreSQL (performance tuning, indexing, query optimization) Hands‑on experience with Airflow for orchestration Deep SQL knowledge and experience designing dimensional models Experience working with transformation frameworks (SQLMesh, dbt, or similar) Experience integrating SaaS tools such as HubSpot or Aircall into analytics environments Strong stakeholder communication skills, especially with Marketing or Growth teams Preferred Qualifications Experience supporting BI tools such as Sigma Background in marketing analytics (attribution, CAC, LTV, funnel analysis) Experience working in analyst‑heavy teams Familiarity with Python for pipeline utilities or automation What Success Looks Like (2–5 Months) Reliable ingestion from Aircall, HubSpot, and internal systems Clear, governed marketing data models used consistently in Sigma Noticeable improvement in query performance and data reliability Reduced ad hoc firefighting and stronger pipeline observability Analysts able to self‑serve confidently on curated datasets #J-18808-Ljbffr