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Large reputable employer, generalist data-engineer title, metro location increase candidate density.
Core data engineering skills (ETL, Spark, AWS) are highly transferable across industries.
Technical stack requirements (Spark, JVM, Python, AWS) present but no explicit years, yielding moderate filtering.
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Develop and maintain production-grade big-data ingestion pipelines supporting advanced analytics projects on on-premises and cloud data lakes.
Collaborate with cross-organizational teams to define data ingestion, validation, cleansing, and enrichment requirements and ensure usable final datasets for domain experts and data scientists.
Implement automated data validation, profiling, cleansing, and enrichment using agile development and CI/CD practices; participate in code reviews and DevOps support activities.
Bachelor’s degree in computer science, engineering, mathematics, or related technical discipline.
Proficiency in at least one JVM language (Java, Scala, or Kotlin) and one interpreted declarative language such as Python.
Hands-on experience with big data engineering concepts, ETL/ELT pipelines, and technologies like Apache Spark, S3, and Delta Lake.
Entry-level experience with AWS platform services including S3, EC2, DMS, RDS, EMR, Redshift, Lambda, DynamoDB, CloudWatch, and CloudTrail.
Candidate comfortable working in agile, CI/CD-driven environments focused on data pipeline development and automation.
Experience collaborating across multiple teams including data source teams, domain experts, and data scientists to deliver validated, enriched data.
Technical aptitude with modern big-data ecosystems, cloud services, and software engineering best practices suited for production analytics platforms.