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Generalist data engineering role, metro location, and mid-level experience increase applicant competition.
Data engineering and cloud platform skills are highly transferable across industries.
Broad mandatory cloud, streaming, ETL, and warehouse skillset increases filter strictness moderately.
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Design, develop, and optimize scalable ETL/ELT pipelines for both batch and real-time data processing.
Build and maintain enterprise data warehouses, data lakes, and modern cloud-based data platforms across Google Cloud, Azure, and AWS environments.
Ensure data integration, quality, governance, and performance tuning to enable high-performance analytics and business intelligence solutions.
3–11 years of professional experience in Data Engineering, Data Warehousing, or Big Data technologies.
Bachelor's degree in Computer Science, IT, Data Engineering, Software Engineering, or related field.
Hands-on experience with cloud data platforms including Google BigQuery, Dataflow, Dataproc, Pub/Sub; Azure Synapse, Data Factory; and AWS Redshift, Glue.
Proficiency in SQL and Python programming; experience with Apache Spark and Apache Kafka; and skills using dbt or Oracle Data Integrator (ODI) for data transformation.
Experienced in designing and managing cloud-native data lakehouse and data warehouse architectures across multiple cloud platforms (Google Cloud, Azure, AWS).
Strong background in both batch and real-time data pipeline development, with expertise in Apache Spark and Kafka reflecting operational and performance optimization focus.
Familiarity with data governance, metadata management, data quality frameworks, and ability to contribute to platform modernization and architecture discussions.