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Mid-level data engineering role, metro location, and common skillset increase applicant competition.
Core data engineering skills transferable across industries despite industrial-domain focus.
Explicit 3–7 years and mandatory Spark, SQL, Python, cloud and orchestration skills create strict filters.
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Design, build, and maintain scalable, reliable batch ETL/ELT and CDC data pipelines powering AI, ML, and analytics for industrial asset monitoring.
Own data ingestion from client systems and transform raw data into trusted analytical and machine learning-ready datasets with focus on data quality and observability.
Collaborate with ML, product, and engineering teams to optimize Spark/PySpark and SQL workloads, storage formats, and implement monitoring and alerting mechanisms.
3–7 years of hands-on experience building production-grade data pipelines.
Strong expertise in SQL (query optimization and complex transformations) and Python programming for data engineering.
Experience with distributed processing frameworks like Spark/PySpark and workflow orchestration tools such as Dagster, Airflow, or Prefect.
Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; Experience with AWS or Azure cloud platforms.
Experienced data engineer skilled in building scalable, idempotent, incremental, and backfill-safe data pipelines in production environments.
Comfortable operating within hybrid cloud platforms supporting large-scale data processing for analytics and ML use cases.
Able to drive best practices for scalability, reliability, maintainability, and data quality in collaboration with cross-functional teams including ML and product groups.