About this role
Lead Data Engineer
Summary
Lead Data Engineer
Title: Lead Data Engineer
ID: 731229130
Department: Staff
Location: Remote
Salary Range: $140,000 – $180,000 base salary, based on experience, knowledge, certifications, and market alignment.
Salary Range Note: N/A
Workplace Type: Remote
Firm Overview
Hinshaw & Culbertson LLP is a national law firm with more than 500 attorneys and professionals across the United States. Founded in 1934 and headquartered in Chicago, the firm provides sophisticated legal counsel to clients in highly regulated industries, including insurance, financial services, healthcare, and professional services, as well as to government agencies, educational institutions, and nonprofit organizations. Learn more at hinshawlaw.com.
Job Summary
The Lead Data Engineer is a hands-on technical leader responsible for designing, building, and operating enterprise-grade data integration and analytics platforms across the firm. This role owns core data pipelines, data models, and production data operations, and establishes engineering standards that improve reliability, data quality, and delivery speed. The Lead Data Engineer partners closely with IT, Information Security, Information Governance, Finance, Knowledge Management, Marketing/Business Development, and practice groups to reduce data silos, clarify data ownership, and deliver trustworthy data for reporting, analytics, and emerging AI initiatives.
Essential Job Functions
- Enterprise Data Integration & Pipelines: Design, build, and maintain robust ETL/ELT pipelines that integrate data from core firm systems (finance, HR, CRM, document management, and other enterprise platforms). Ensure data is delivered accurately, securely, and on schedule.
- Data Architecture & Modeling: Define and enforce data architecture standards, including data models, schemas, and storage patterns for cloud-based data warehouse and lakehouse platforms. Optimize for performance, scalability, and analytical use.
- Cloud Data Platform Management: Lead the implementation and operation of Microsoft Azure-based data services, including Azure Data Factory, Azure Databricks, Azure Data Lake Storage, and Azure Synapse Analytics. Manage development, test, and production environments with focus on cost efficiency and reliability.
- Own the firm’s enterprise data warehouse and lakehouse platforms, including data models, ingestion pipelines, performance, reliability, and ongoing operational health.
- DevOps / DataOps Practices: Implement source control, branching strategies, CI/CD pipelines, automated testing, deployment workflows, monitoring, and alerting for data pipelines and related code.
- Production Data Operations: Oversee scheduling, execution, reconciliation, and monitoring of production data workflows. Proactively identify and resolve failures, data quality issues, and performance bottlenecks before they impact reporting or operations.
- Data Governance & Quality: Partner with Information G