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Metro location, popular data-engineer role, and broad common-skill requirements increase competition.
Strong Databricks, cloud and regulated-environment experience moderately limits cross-industry transferability.
Explicit 6–8 years requirement plus mandatory Databricks and cloud production experience makes filtering strict.
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Design, build, and operate scalable, cloud-native data pipelines and platforms primarily on AWS using Databricks and related technologies.
Ensure production readiness of data solutions with focus on security, testing, observability, cost-efficiency, performance, reliability, and data quality.
Collaborate with cross-functional teams including analysts, data scientists, and AI engineers; mentor junior engineers and contribute to engineering standards.
6-8 years of hands-on experience in data engineering roles.
Strong practical experience with Databricks, Delta Lake, and Medallion Architecture.
Proven ability to design, build, and support scalable data pipelines and data products from enterprise source systems.
Experience with cloud platforms, primarily AWS; exposure to Azure where required.
Experienced senior engineer focused on delivery and ownership of complex data engineering challenges in enterprise or regulated settings.
Proficient in implementing data quality controls, Master Data Management, and automating data completeness reporting, including AI-assisted capabilities.
Experienced in industrialising AI/ML workloads and operationalising data solutions from proof-of-concept to production.