Data Engineer (Consulting / Enterprise Financial Services) Location: 100% Remote (US-based – EST Hours) Engagement Type: Long-Term Contract (1+ Years, W2) Compensation: $60.00 – $65.00/hr W2 Work Authorization: US Citizens or Green Card holders only (W2 requirement)

Overview

We are seeking an experienced Data Engineer to join a leading global professional services and consulting advisory practice, supporting a high-profile enterprise banking and financial services client. In this role, you will drive a major enterprise-wide data modernization initiative aimed at breaking down scattered, siloed data repositories and transforming them into accessible, high-quality, and unified data assets across the organization.

Key Responsibilities

Develop and maintain scalable data pipelines using Python and advanced SQL. Integrate, process, and validate large-scale datasets (handling volumes exceeding 1B+ records). Implement and enforce data governance, data quality management, and automated reconciliation frameworks. Participate in and conduct rigorous code reviews to maintain high coding and architectural standards. Collaborate with cross-functional technical teams and client stakeholders to make data accessible, reliable, and usable firm-wide.

Required Qualifications

Core Technical Skills: Strong hands-on proficiency with Python and SQL for data engineering, pipeline development, and transformation. Large-Scale Data Experience: Proven track record integrating and validating massive datasets (1B+ records). Data Quality & Governance: Deep understanding of data quality frameworks, lineage, metadata, and governance best practices. Engineering Best Practices: Experience conducting code reviews, version control (Git), and standard CI/CD practices. Strongly Preferred Hands-on experience with modern cloud data platforms: Databricks and/or Snowflake. Exposure to building or supporting data visualization / reporting layers (e.g., Tableau, Power BI, or QuickSight). Prior experience in financial services, banking, or enterprise consulting environments.