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Remote US-collaboration and a mid-level generalist data engineer profile increase applicant competition.
Requires specialized cloud data engineering, Snowflake/Databricks, streaming and CDC experience not easily transferable.
Explicit 6–8+ years plus mandatory Snowflake/Databricks, AWS, Python/PySpark, and streaming requirements.
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Lead development, optimization, and maintenance of scalable data pipelines supporting real-time analytics, AI/ML initiatives, and enterprise reporting.
Implement and manage AWS-based data lake architectures, streaming ingestion with Kinesis or Kafka, and automated data quality validation within pipelines.
Provide technical mentoring to junior engineers and collaborate cross-functionally with Product and ML teams to translate designs into functional data solutions.
6–8+ years of data engineering experience focusing on large-scale distributed systems.
Expert-level skills in Python, PySpark, and SQL.
Hands-on experience with Snowflake or Databricks within AWS ecosystem, including building streaming applications using Kinesis or Kafka.
Work Hours: Must be available during core U.S. collaboration hours (approx. 6 AM–2 PM IST).
Technical leader with demonstrated ownership of end-to-end data pipeline QA and operational discipline to ensure production-grade data systems.
Experienced in engineering ML-ready datasets, feature stores, and operationalizing ML workflows within modern cloud data platforms.
Proven ability to collaborate effectively across Product, Engineering, Analytics, and ML teams in a remote, fast-paced SaaS environment.