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Remote, mid-level Data Engineer with common big-data stack attracts many qualified applicants.
Core data engineering skills (Spark, Kafka, dbt, data warehousing) are highly transferable across industries.
Explicit 4+ years requirement plus mandatory Spark/Kafka/dbt/BigQuery and platform experience increases filter strictness.
Design, build, and maintain scalable batch and real-time data pipelines and cloud-native data platforms using technologies including Maxwell, Kafka, Spark, S3, Trino, BigQuery, and dbt.
Develop and optimize data models following Medallion Architecture to create reliable, reusable, high-quality datasets powering analytics and business-critical applications.
Own end-to-end lifecycle of critical data pipelines ensuring high availability, monitoring, SLA adherence, data quality, and proactive incident resolution.
Minimum 4+ years of hands-on experience designing and building scalable data platforms, data lakes, and data warehouses.
Strong proficiency in Apache Spark (Scala, Python), SQL, Kafka, CDC/Maxwell, dbt, and experience with cloud platforms such as Amazon S3, BigQuery, and Trino.
Experience designing dimensional data models, Medallion Architecture implementation, and building data marts for analytics and BI.
Work Experience Required: 4+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in building and optimizing large-scale, distributed data processing systems focusing on performance, reliability, cost efficiency, and observability.
Comfortable working cross-functionally with Product Managers, Data Analysts, Backend Engineers, and stakeholders to translate business needs into scalable data solutions.
Skilled in implementing engineering best practices including CI/CD, automation, testing frameworks, and documentation to improve developer productivity and platform reliability.