• Design, develop, and optimize scalable data pipelines using Databricks (PySpark, Delta Lake, Unity Catalog, Lakeflow) and AWS (S3, Glue, Lambda, Step Functions, Redshift)
• Lead data ingestion, transformation, and modeling initiatives for enterprise data platforms.
• Define and implement robust data models supporting analytics, reporting, and AI/ML use cases.
• Gather and translate complex business requirements into scalable technical solutions.
• Establish data quality, monitoring, testing, and operational best practices across data platforms.
• Mentor engineers, drive architecture standards, and lead end-to-end solution delivery.
• Support strategic initiatives including AI readiness, data unification, metadata management, and enterprise integration programs.
Required Skills & Experience
• 8+ years of experience in Data Engineering, Data Architecture, or large-scale distributed data systems.
• Expert knowledge of AWS Data Services and Databricks/Spark ecosystem.
• Strong expertise in data modeling (Dimensional, Canonical, Data Vault, Domain-Driven).
• Advanced SQL and Python development skills with ETL/ELT experience.
• Experience with CI/CD, GitHub, DevOps practices, automated testing, and production deployments.
• Proven ability to work independently and lead solutions in ambiguous business environments.
Preferred Qualifications
• Experience in Asset Management, Wealth Management, or Financial Services.
• Knowledge of data quality frameworks, metadata management, dbt, semantic layers, or data mesh concepts.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience)

AWS Databricks Data Engineer

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