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Tier-1 brand, mid-level data engineer role in a metro with broad platform requirements increases applicant density.
Role requires specialized data platform, cloud, and streaming skills, limiting easy cross-industry transferability.
Explicit 5+ years plus many mandatory platform, language, and governance skills creates strict shortlisting filters.
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Design, build, and maintain scalable batch and real-time data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink.
Develop and oversee data platform components for cataloging, quality, governance, lineage, and semantic/metrics layers.
Lead technical solution evaluations, drive architecture decisions, and foster adoption of new technologies across software engineering teams.
5+ years of applied software engineering or data platform development experience with formal training or certification.
Hands-on experience with Databricks data pipelines and orchestration tools like Apache Airflow.
Proficiency with AWS cloud data services (S3, Glue, Redshift, Athena, EMR, Lake Formation) and programming in Python and SQL.
Experience with distributed data processing frameworks (Apache Spark, Flink) and data modeling techniques (star schema, snowflake).
Experienced in building secure, scalable, production-grade data platforms with governance and data lineage on Kubernetes environments.
Capable of collaborating with business stakeholders and analytics teams to translate data needs into production solutions.
Skilled in evaluating external and internal technology solutions and leading technical communities to advance architecture and technology adoption.