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Common mid-level data engineering skills, metro hybrid role, and known employer create high competition.
Core data engineering skills are highly transferable across industries, so sensitivity is low.
Mandatory five years plus specific data engineering skills makes shortlisting moderately strict.
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Design, develop, and maintain scalable data warehouse and Lakehouse platforms using ETL/ELT pipelines for trusted data assets.
Partner with architects and senior stakeholders to define data architecture standards, reusable engineering practices, and develop conceptual to physical data models for enterprise data domains.
Implement data quality, validation, observability, security, and governance controls while supporting batch and near-real-time data processing scenarios.
Bachelor’s degree in computer science, engineering, information systems, or equivalent experience.
Minimum 5 years of experience in software engineering, data engineering, analytics engineering, or enterprise data platform delivery.
Experience with SQL, Python, ETL/ELT pipeline development, and data modelling including conceptual, logical, physical, normalized and dimensional modelling.
Experience or proficiency with Databricks data warehouse/Lakehouse platforms and AWS cloud data services preferred but not strictly mandatory.
Experienced with enterprise-scale data platform delivery, especially using Databricks and AWS data services in a data architecture or data engineering role.
Strong in developing reusable, governed data assets and implementing data quality, validation, monitoring, and governance frameworks in complex data ecosystems.
Able to collaborate with architects, engineers, and business stakeholders to translate platform needs into actionable designs and mentor teams on best coding and engineering practices.