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Remote role, generalist Data Engineer title, and 5+ years mid-level range drive high applicant competition.
Specialized cloud data stack and streaming experience make cross-industry transitions less transferable.
Mandatory 5+ years plus required Snowflake/Databricks, AWS, streaming and data QA create high technical screening.
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Build, optimize, and maintain scalable data pipelines and AWS data lake infrastructure supporting real-time analytics, AI/ML, and enterprise reporting.
Own end-to-end data quality by implementing automated validation, schema governance, and proactive monitoring of large-scale data ecosystems.
Collaborate closely with Product, ML, and Engineering teams to translate architectural designs into production-grade data solutions and participate in on-call rotations for production incident response.
5+ years of experience in data engineering focused on large-scale distributed systems.
Expert-level skills in Python, PySpark, and strong SQL proficiency.
Hands-on experience with Snowflake or Databricks within AWS, plus real-time streaming experience with AWS Kinesis or Kafka.
India-based remote work with critical overlap hours aligned to US timezones; on-call availability required.
Proven ability to build and maintain complex, event-driven data pipelines and data lakes using modern cloud technologies and advanced modeling techniques.
Experienced in operationalizing ML workflows and engineering ML-ready datasets within a production environment.
Strong engineering discipline with ownership mindset, including focus on data quality, pipeline observability, and cross-functional collaboration across technical and non-technical stakeholders.