Senior Data Engineer Enterprise AI / Analytics Enablement

Senior Data Engineer Enterprise AI / Analytics Enablement

Location: NYC (Hybrid; 3 4 days onsite)
Start: Immediate / January
Duration: 3+ Months
Employment Type: W-2 ONLY

Role Overview

We are seeking a hands-on, execution-focused Senior Data Engineer to support a high-visibility enterprise AI initiative for a major public-sector client. This role is data-first, not application-first, and focuses on ingestion, transformation, validation, and operational readiness of data feeding analytics dashboards and AI systems.

This is a heads-down delivery role , The expectation is immediate impact with minimal ramp-up.

• What You Will Do
• Build, maintain, and troubleshoot ETL / ELT data pipelines
• Clean, normalize, validate, and structure data for:
  • • analytics dashboards
    • AI / LLM consumption
    • UAT and audit-ready workflows
• Work with structured and semi-structured data (tables, logs, extracted document data)
• Partner closely with:
  • • AI engineers (ensuring data is AI-ready)
    • Application engineers (ensuring data is consumable)
• Support UAT execution, including test-data preparation and defect triage
• Ensure traceability and audit readiness from source pipeline output
• Debug data quality and pipeline issues under tight timelines

Required Experience (Non-Negotiable)

• 6+ years of hands-on data engineering experience
• Strong SQL (writing, debugging, optimizing complex queries)
• Proven ownership of production data pipelines
• Experience with one or more modern data platforms, such as:
  • • Azure Data Factory / Synapse / Databricks
    • AWS Glue / Redshift
    • Snowflake / BigQuery
    • Informatica / Talend
• Experience supporting analytics or dashboarding layers (Power BI, Tableau, Looker, etc.)
• Comfortable operating in fast-paced consulting environments
• Able to deliver clean outputs with partial or evolving requirements

Strongly Preferred

• Experience preparing data for AI / ML or GenAI workflows
• Experience with OCR-extracted or document-derived data
• Familiarity with Azure environments
• Prior experience on regulated or public-sector projects
• Experience working alongside McKinsey, Big-4, or similar consulting teams

What This Role Is NOT

• Not a frontend or UI role
• Not a data science or modeling role
• Not a research or experimentation role
• Not reporting-only or analyst work

This is engineering and execution, not theory.

What Success Looks Like

• Data pipelines are stable, auditable, and trusted
• Dashboards and AI systems receive clean, validated inputs
• UAT sessions run smoothly with minimal data surprises
• The team is never blocked waiting on data