Lead DATA ENGINEER - Cloud Migration

TechDigital Group; Jersey City (NJ); On-site; USD 100,000 - 130,000; Full time

Job summary

TechDigital Group is looking for a skilled data engineer in Jersey City, NJ, to lead the migration of legacy data platforms to cloud-native solutions using Databricks. The ideal candidate will have 8–12 years of experience in data engineering and strong expertise in Apache Spark and PySpark. Responsibilities include designing Databricks architectures, ensuring data quality and governance, and implementing cloud security measures. This role offers the opportunity to work on large-scale transformative projects in a collaborative environment.

Qualifications

• 8–12+ years in data engineering/platform modernization.
• Strong hands-on experience with Databricks in large-scale enterprises.
• Proven experience delivering cloud migration programs.
• Deep expertise in Apache Spark, PySpark, Spark SQL.

Responsibilities

• Lead and execute migration of legacy data platforms to Databricks.
• Design and implement Databricks Lakehouse architecture.
• Embed data quality and governance controls in migrations.
• Implement cloud security and resilience measures.

Skills

Data engineering experience

Databricks expertise

Apache Spark proficiency

Cloud migration experience

Data quality controls

Job description

Role Overview

This role is part of a multi‑year enterprise initiative to modernize data platforms by migrating from legacy and on‑prem environments to cloud‑native, governed, and scalable architectures. The role focuses on migrating enterprise data workloads to Databricks on strategic cloud platforms, enabling standardized data engineering, analytics, centralized reporting, reconciliation utilities, and AI/ML use cases—while adhering to controls, security, resilience, and regulatory compliance.

Key Responsibilities

• Lead and execute migration of legacy data platforms (on‑prem / non‑standard tools) to Databricks on cloud under the Olympus program
• Perform application, data, and pipeline refactoring to cloud‑native Databricks patterns
• Drive migration planning including dependency analysis, sequencing, and cutover strategy
• Support coexistence models and transition from dual‑run to cloud‑only execution

Databricks Lakehouse Engineering

• Design and implement Databricks Lakehouse architecture (Bronze / Silver / Gold)
• Build scalable batch and streaming pipelines using PySpark, Spark SQL
• Leverage Delta Lake for reliability, versioning, and performance
• Optimize compute usage and cost in line with enterprise cloud efficiency goals

Enterprise Data Controls & Governance

• Embed data quality, reconciliation, and completeness controls as part of migration
• Ensure migrated workloads meet EDO governance, MCA, and audit requirements
• Maintain lineage, traceability, and explainability across migrated assets
• Support risk‑critical use cases (Finance, Ops, Recon, Reporting)

Cloud Security & Resilience

• Implement cloud‑aligned RBAC, identity controls, and secure access patterns
• Enforce data encryption, masking, and classification standards
• Ensure workloads meet operational resilience and recovery expectations
• Partner with cloud platform and security teams for certification and sign‑off

Reporting, Analytics & AI Enablement

• Enable downstream BI, regulatory reporting, and MI workloads on Databricks
• Support centralized reporting programs (e.g., ARA, GRU‑related use cases)
• Prepare data foundations for AI / ML and Agentic workflows post‑migration

Required Qualifications

• 8–12+ years in data engineering / platform modernization
• Strong hands‑on experience with Databricks in large‑scale enterprises
• Proven experience delivering cloud migration programs (on‑prem → cloud)
• Deep expertise in Apache Spark, PySpark, Spark SQL
• Experience embedding controls, reconciliation, and data quality in migrations
• Experience in regulated environments (banking / financial services preferred)

Preferred Qualifications

• Experience with Client Olympus or equivalent enterprise cloud programs
• Knowledge of legacy data platforms and modernization patterns
• Familiarity with Finance, Ops, Recon, or Balance‑Sheet data domains
• Exposure to MLflow, AI pipelines, or GenAI enablement on cloud
• Strong understanding of run‑the‑bank vs change‑the‑bank execution

Behavioral & Delivery Expectations

• Strong ownership and execution mindset
• Comfortable operating in large, multi‑vendor transformation programs
• Ability to engage with Technology, Operations, Risk, and Audit stakeholders
• Disciplined approach to migration risk, controls, and documentation