Description

Key Responsibilities

• 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

• 12+ 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)

Key Skills

Education

Bachelor's degree