VP, Data Engineering Technical Lead
Core
Lead the modernization of legacy data systems into a cloud-native Lakehouse environment powering analytics, AI, and business intelligence.
Role type
VP, Data Engineering Technical Lead
Builds
Scalable data pipelines, reusable data design patterns, and AI-integrated operational intelligence for a financial services enterprise.
Domain
Financial Services / Cloud Data Engineering
Deliverable
production ML models | product features | infrastructure
Required skills
Data pipeline architecture, legacy ETL modernization, data design patterns (CDC, SCD, Medallion), POC/POV development, AI-assisted engineering, Spark optimization, CI/CD enforcement, team mentorship
Preferred skills
Experience with Delta Live Tables, Iceberg, streaming ingestion, AI-driven observability
Technologies
Databricks, Apache Spark, dbt, Fivetran, Airflow, Kafka, Azure, GCP, Synapse, Data Factory, BigQuery, Pub/Sub, SSIS, SSRS
Responsibilities
Modernize legacy ETL pipelines to cloud-native architectures; Architect reusable data design patterns across ingestion and consumption layers; Develop and lead POCs/POVs for emerging technologies; Leverage AI tools for code generation and pipeline optimization; Apply ML for operational intelligence to detect anomalies; Enforce engineering excellence via CI/CD and observability; Mentor data engineers on best practices; Optimize performance and cost of Spark workloads.
Seniority
VP, hands-on technical leadership