Back
High due to Tier-1 brand, broad senior data role, and metro/remote applicant reach.
Medium because core data engineering skills transfer, but SaaS revenue and product specifics increase domain bias.
High because of explicit 8+ years requirement and many mandatory data platform technologies.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build scalable end-to-end data architecture integrating product telemetry, customer lifecycle, and revenue metrics as a trusted single source of truth.
Develop and maintain distributed real-time and batch data pipelines unifying product usage, CRM, and billing for revenue attribution and machine learning feature pipelines.
Lead implementation of telemetry frameworks, ensure data governance, reliability, privacy compliance, and support data-driven decision-making across the organization.
8+ years of experience in Data Engineering or Distributed Systems.
Proficiency in building production-grade data pipelines using Python and advanced SQL with performance and reliability focus.
Experience operating modern data warehouses (e.g., Snowflake, Databricks) and orchestration tools (e.g., Airflow, Dagster).
Work Experience Required: 8+ years in relevant data engineering roles.
Experienced in leading design and operation of large-scale production data platforms with measurable business impact in SaaS or product-led growth environments.
Skilled in integrating diverse data sources and applying data modeling best practices (e.g., using dbt) for self-service analytics.
Able to support ML workflows through feature engineering, training datasets, and real-time scoring infrastructure, with ability to adopt emerging AI-assisted development tools.