Back
Hybrid/remote role with broad, generalist data engineering requirements increases applicant competition.
Core data engineering skills are transferable, though SaaS product analytics experience increases specialization.
Explicit 8+ years plus mandatory data platform, orchestration, and modeling tool experience creates strict screening.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build end-to-end data architecture integrating product telemetry, customer lifecycle, and revenue metrics into a trusted single source of truth.
Develop and maintain scalable distributed real-time and batch data pipelines connecting product usage, CRM, and billing for closed-loop revenue attribution.
Establish reliability, governance, and data quality frameworks supporting machine learning feature pipelines and standardized telemetry frameworks across platforms.
8+ years of experience in Data Engineering or Distributed Systems.
Proven experience designing and operating large-scale data platforms in production with measurable business impact.
Proficiency in building production-grade data pipelines with Python and advanced SQL; experience with modern data warehouses (e.g., Snowflake, Databricks) and orchestration tools (e.g., Airflow, Dagster).
Experience integrating event telemetry, CRM, and billing data sources; applying data modeling best practices using dbt or equivalent tools.
Experienced with SaaS or product-led growth environments, familiar with product analytics platforms or streaming systems.
Skilled in supporting machine learning workflows including feature stores, training pipelines, or real-time scoring infrastructure.
Adopts AI-assisted development tools and emerging engineering workflows to enhance team productivity and deliver reusable automation.