Data Engineering Lead

This role seeks an experienced Technology Manager with strong expertise in Marketing Data Platforms, Data Architecture, Solution Architecture, Cloud & AI Technologies, and Team Leadership. Primary responsibilities include leading delivery teams, gathering business and technical requirements, designing scalable and secure cloud-based solutions, and driving end-to-end architecture decisions. Based in NJ/NY with immediate start; one position available.

Responsibilities

• Lead end-to-end delivery of Marketing Data platform, Integration, Reporting, and Architecture initiatives from intake through production deployment.
• Partner with business stakeholders to clarify requirements, define scope, validate priorities, and translate objectives into executable delivery plans.
• Manage project risks, dependencies, decisions, timelines, and tradeoffs across business, development, architecture, infrastructure, and operations teams.
• Establish and maintain delivery artifacts including project plans, RAID logs, decision logs, requirements documentation, release plans, and production handoff materials.
• Drive sprint planning, backlog refinement, delivery ceremonies, technical reviews, and issue-resolution discussions.
• Ensure solutions are scalable, secure, supportable, well-documented, and aligned with enterprise data governance standards.
• Provide technical leadership across cloud data platforms, data modeling, data pipelines, integrations, and downstream reporting or activation use cases.

Requirements

• 10 to 15 years of experience in Data Engineering, Data Modeling & Architecture with strong foothold in Azure and Databricks.
• Deep experience with Marketing Data concepts: Account, Customer, Prospect, Lead, Opportunity, Engagement, Consent, etc.
• Strong in Customer 360 and MDM concepts with multiple system integrations.
• Strong communication skills and positive attitude; good problem solver.
• Lead and manage a team of 5–8 or more Data Engineers, Architects, and technical specialists.

Key Focus Areas

• Delivery Leadership: Planning, Risks, Dependencies, Status, Escalations
• Business Partnership: Requirements, Scope, Prioritization, Stakeholder Communication
• Technical Leadership: Azure, Databricks, SQL/RDBMS, Git, Integration patterns, Customer 360/MDM, Data Modeling, Copilot, Claude, GitHub Copilot
• Data Governance: Quality, Controls, Lineage, Documentation, Data Privacy & Security
• Team Leadership: Mentoring, Agile ceremonies, Design reviews
• Domain: Marketing (B2B), Analytics