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Mid-level generalist data engineer role with common tech stack and hybrid/metro location increases competition.
Core data engineering skills (ETL, SQL, Python, pipelines) are highly transferable across industries.
Explicit 3–5 year requirement plus mandatory Python, SQL, AWS and streaming skills raise filtering strictness.
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Build and maintain centralized data systems including data pipelines, warehouses, data lakes, and hubs optimizing extraction, transformation, and loading (ETL) from varied data sources.
Develop analytics tools and reports leveraging data pipelines to generate actionable insights on customer acquisition, operational efficiency, and key business metrics.
Support cross-functional teams by resolving data-related technical issues and ensuring secure data management across multiple AWS regions and data centers.
3 to 5 years of Data Engineering experience.
Degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
Strong Python and advanced SQL skills; experience with big data technologies (including AWS, noSQL databases like Elasticsearch, event-streaming platforms such as Kafka).
Experience building and optimizing ETL pipelines and data architectures; familiarity with Linux environment.
Proven experience managing complex, large-scale data infrastructures and pipelines in dynamic, cross-functional environments.
Ability to perform root cause analysis and to pragmatically evaluate engineering trade-offs for data solutions.
Strong analytical capabilities with complex and unstructured datasets, combined with effective communication and project management skills.