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Tier-1 brand, metro role, senior data engineer skills, and broad cloud/Databricks requirements increase competition.
Strong data engineering technical requirements make skills transferable, though finance experience is preferred.
Explicit 8–11 years plus mandatory Databricks, AWS, Spark, and Python creates strict screening.
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Lead design and governance of scalable cloud data architectures and engineering standards focused on analytics and investment management needs.
Design and build ETL/ELT pipelines using Python, Spark, and Databricks, ensuring data quality, security, and platform reliability.
Provide technical leadership and mentoring across engineering teams while driving cloud modernization and CI/CD best practices.
8-11 years of experience in Data Engineering, Data Architecture, or related fields.
Proven hands-on expertise with AWS, Databricks, Python, Spark, SQL, data modeling, and cloud-based data platform design.
Experience with data warehousing, Lakehouse architecture, and implementing CI/CD and DevOps practices.
Work Experience Required: 8-11 years; Financial Services or Asset Management experience preferred but not mandatory.
Experienced leader capable of both hands-on data engineering and strategic architectural decision-making.
Strong technical background in cloud data platforms, especially AWS and Databricks, with deep knowledge of modern data pipeline construction.
Ability to influence and mentor teams while managing stakeholder expectations in enterprise or financial services environments.