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Mid-level (5–8 yrs) generalist data engineering role with broad toolset increases applicant density.
Data engineering skills and tooling are broadly transferable across industries.
Explicit 5–8 years requirement plus mandatory data platform skills and specific tooling creates strict filtering.
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Lead design and build of foundational zero-to-one data platform infrastructure and pipelines from scratch.
Manage production application data streams ensuring low-latency synchronization between transactional (OLTP) and analytical (OLAP) systems using CDC.
Build and maintain robust AI/ML data infrastructure for model training, feature engineering, and real-time event streaming.
5-8 years of experience in data engineering, backend engineering, or data platform engineering with proven zero-to-one project delivery in fast-paced or startup environments.
Strong hands-on programming skills in Python and SQL.
Experience with live application databases (e.g., PostgreSQL, MongoDB) and implementing Change Data Capture (CDC) pipelines.
Familiarity with cloud platforms (AWS, GCP, or Azure) and tools like Airflow, Spark, Kafka, Flink, or dbt.
Experienced in architecting and scaling end-to-end production data pipelines integrating transactional and analytical systems in real-time.
Comfortable leading cross-functional collaboration to translate ambiguous business needs into production-ready data solutions.
Background in building AI/ML data pipelines that support feature engineering, experimentation, and model monitoring.