Lead Data Engineer

Hinshaw & Culbertson•Chicago, IL

18d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.
8+ years of hands-on experience in data engineering, ETL/ELT development, and data integration.
3+ years of experience designing and operating cloud-based data platforms (Microsoft Azure preferred).
Strong MS SQL skills and experience with relational database design, development, and performance tuning (SQL Server preferred).
Experience building reliable production data pipelines, including monitoring, alerting, and reconciliation.
Experience with source control and deployment practices for data workflows (e.g., Git and CI/CD pipelines).
Excellent written and verbal communication skills, including ability to explain technical concepts to non-technical audiences.
Ability to handle sensitive and confidential information with discretion and professionalism.
Availability for occasional after-hours work for major releases or production incidents.

Nice To Haves

Advanced experience with Azure data services (ADLS, Databricks, Azure Data Factory, Synapse).
Experience implementing enterprise data warehouses, data marts, and lakehouse architectures.
Experience with data integration tools such as Azure Data Factory, Talend, Boomi, or similar platforms.
Strong scripting and programming skills (Python, PowerShell, and/or .NET/C#) for data processing and custom integrations.
Familiarity with business intelligence and analytics tools such as Power BI or Tableau.
Experience with data governance frameworks, data quality controls, data cataloging, and lineage documentation.
Experience in a law firm or professional services environment is a plus, including familiarity with systems such as Elite 3E, Aderant, iManage, NetDocuments, Intapp/InterAction, or similar platforms.
Relevant certifications (e.g., Microsoft Certified: Azure Data Engineer Associate) are a plus.

Responsibilities

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.
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.
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.
Implement source control, branching strategies, CI/CD pipelines, automated testing, deployment workflows, monitoring, and alerting for data pipelines and related code.
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.
Partner with Information Governance and Security teams to implement data quality standards, lineage documentation, reconciliation processes, and stewardship models. Help define and enforce “source of truth” ownership for critical datasets.
Work with business stakeholders to translate requirements into technical solutions and communicate complex data concepts in clear, non-technical terms. Ensure solutions align with firm strategy and operational priorities.
Ensure data integration and storage solutions comply with firm security policies, client confidentiality requirements, and applicable regulatory obligations. Implement access controls, encryption, and auditing where appropriate.
Evaluate emerging data, analytics, and AI capabilities and lead proof-of-concepts that improve reliability, usability, and business value.
Provide design guidance, code reviews, and technical coaching to junior engineers or database professionals as applicable. Establish repeatable patterns and standards.
Lead data-focused projects from initiation through delivery, coordinating internal teams and external vendors/consultants. Ensure knowledge transfer and long-term sustainability.