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Remote role, popular mid-level Data Engineer title, and 3-6 year requirement amplify applicant competition.
Skills like Spark, Kafka, dbt, and BigQuery are transferable, though delivery-domain knowledge moderately preferred.
Explicit 4+ years and mandatory Spark, Kafka, dbt, BigQuery and CI/CD requirements imply high strictness.
Own design, development, and maintenance of scalable batch and real-time data pipelines using technologies like Maxwell, Kafka, Spark, and dbt to support analytics and business-critical applications.
Build and optimize cloud-native data platforms (S3, Spark, Trino, BigQuery) and data models following Medallion Architecture to create reliable, reusable datasets for Product, Analytics, and Business teams.
Ensure end-to-end pipeline lifecycle management including high availability, monitoring, SLA adherence, data quality, security, and automation to improve platform reliability and developer experience.
Minimum 4+ years of hands-on experience in designing and building scalable data platforms, data lakes, and data warehouses.
Strong proficiency in Spark (Scala, Python) and SQL with production-grade data pipeline experience and distributed data processing knowledge.
Experience with batch and streaming pipelines using Kafka, CDC/Maxwell or similar event-driven architectures.
Experience with cloud-native data platforms such as Amazon S3, BigQuery, Trino, and hands-on dbt development including testing and documentation.
Experienced with modern data lake architectures and dimensional data modeling (Medallion Architecture, star schemas) supporting analytics and BI.
Skillful in optimizing large-scale Spark jobs, SQL queries, and distributed processing for performance, scalability, and cost efficiency.
Able to independently design, build, and manage critical data infrastructure while collaborating cross-functionally with Product, Engineering, Analytics, and Business stakeholders.