Lead Data Engineer

Hinshaw & Culbertson LLP is a national law firm with over 500 attorneys across the United States. Founded in 1934 and headquartered in Chicago, we provide sophisticated legal counsel to clients in highly regulated industries, including insurance, financial services, healthcare, and professional services. We also offer strategic business and transactional advice to a wide range of for-profit and nonprofit organizations, including government agencies and educational institutions. Learn more at hinshawlaw.com.

Position Summary:

The Enterprise Data Architect is a hands-on technical leader responsible for designing, building, operating, and governing the firm’s enterprise data platform. The role owns the enterprise data warehouse currently residing on-premise MS SQL and with an eye towards the cloud. The role encompasses lakehouse architecture, data integration, analytics enablement, and production data operations while establishing standards for reliability, security, scalability, and supportability. The position ensures trusted data is exchanged consistently across finance, human resources, client relationship management, document management, Microsoft 365, and other enterprise systems.

This position also serves as the firm’s enterprise data authority. It establishes and maintains authoritative system-of-record designations, enterprise data definitions, metadata standards, master and reference data practices, and data ownership models. Working with technology and business stakeholders, the role resolves cross-system data conflicts, promotes consistent use of trusted data, and supports reporting, business intelligence, operational decision-making, and approved artificial intelligence initiatives. The role leads modernization through Microsoft Fabric, Power BI, Microsoft Purview, Azure data services, and other cloud-based analytics platforms.

Responsibilities:

Enterprise Data Platform Ownership

• Own the architecture, operation, reliability, capacity, lifecycle, and technical roadmap of the enterprise data warehouse, lakehouse, and supporting data services.
• Establish service levels, monitoring, alerting, support procedures, backup and recovery requirements, and operational documentation for critical data platforms.
• Coordinate platform upgrades, releases, incident response, root-cause analysis, and continuous improvement across internal teams and vendors.

Enterprise Data Management and Source-of-Truth Governance

• Define and maintain authoritative systems of record for critical data domains and document how information is created, mastered, synchronized, consumed, retained, and retired.
• Maintain enterprise data definitions, business glossaries, data dictionaries, metadata, lineage, ownership, stewardship assignments, and approved usage rules.
• Lead master and reference data management practices for key entities such as clients, matters, personnel, offices, vendors, financial records, and operational data.
• Partner with business owners to resolve conflicting definitions or records and establish accountable decision processes for data quality and ownership.
• Certify and publish trusted datasets and semantic models for reporting, analytics, integrations, and approved artificial intelligence use cases.

Enterprise Data Integration and Exchange

• Design, build, and operate secure, reliable ETL/ELT pipelines, APIs, file exchanges, event-based integrations, and other data interfaces connecting enterprise systems.
• Define cross-system synchronization patterns, transformation rules, interface contracts, schedules, dependencies, and error-handling standards.
• Monitor data exchanges, reconcile source and target results, resolve failures, and prevent duplicate, incomplete, stale, or inconsistent records.
• Maintain an inventory of integrations and data flows, including ownership, business purpose, source, destination, frequency, sensitivity, and operational support requirements.

Data Architecture and Modeling

• Define data models, schemas, naming conventions, storage patterns, partitioning strategies, and architecture standards for relational, warehouse, lakehouse, and analytics solutions.
• Design solutions for performance, scalability, resiliency, maintainability, and appropriate separation of operational, analytical, and archival workloads.

Microsoft Fabric and Analytics

• Design and administer Microsoft Fabric capabilities, semantic models, and integrations supporting Power BI.
• Enable governed self-service analytics by publishing reusable, documented, and access-controlled data products and semantic models.

Data Quality and Governance

• Establish data quality rules, profiling, validation, reconciliation, exception management, issue tracking, and measurable quality thresholds for critical datasets.
• Implement lineage, cataloging, classification, retention, stewardship, and ownership practices using Microsoft Purview or comparable capabilities.
• Collaborate with business and technical owners to prioritize remediation and measure improvement in data completeness, accuracy, consistency, timeliness, and uniqueness.

Engineering Standards and Automation

• Implement source control, peer review, automated testing, continuous integration and deployment, release management, infrastructure-as-code where appropriate, and repeatable engineering patterns.
• Automate monitoring, quality validation, deployment, reconciliation, documentation, and routine administration to improve reliability and reduce manual effort.

Security and Compliance

• Ensure data platforms and integrations meet firm security policies, client confidentiality requirements, records obligations, and applicable regulatory requirements through access controls, encryption, logging, auditing, segregation of duties, and least-privilege design.
• Partner with Information Security and Governance to support risk assessments, control evidence, data classification, incident response, and compliance alignment, including relevant NIST, CIS Benchmark, and HITRUST practices.

AI and Platform Modernization

• Build secure, governed data foundations that support approved Microsoft Copilot, semantic search, enterprise knowledge discovery, predictive analytics, and other artificial intelligence initiatives.
• Evaluate emerging data technologies and recommend modernization priorities based on business value, risk, cost, supportability, and architectural fit.

Technical Project Leadership

• Lead data projects from requirements through production support; provide architecture, design, and code-review guidance; and mentor technical staff.
• Coordinate vendors and cross-functional teams, communicate recommendations clearly to technical and non-technical stakeholders, and maintain roadmaps, risks, dependencies, and delivery milestones.
• Facilitate data ownership and governance decisions when multiple departments or systems have competing requirements.

Qualifications:

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.
• 8+ years of experience in data engineering, data integration, enterprise data warehousing, and production data operations.
• 3+ years of experience designing or operating cloud-based data and analytics platforms; Microsoft Azure or Microsoft Fabric experience is preferred.
• Advanced experience with Microsoft SQL Server, relational data modeling, development, performance tuning, and enterprise data warehousing.
• Experience defining systems of record, data ownership, enterprise data definitions, master or reference data practices, and cross-system synchronization rules.
• Experience building production data pipelines and integrations with monitoring, alerting, testing, reconciliation, source control, and automated deployment.
• Experience with lakehouse architectures, open data formats such as Delta Lake and Parquet, and enterprise analytics platforms such as Power BI.
• Experience administering or engineering Microsoft Fabric, including OneLake, Data Factory, Data Engineering, Data Warehouse, Lakehouse, Direct Lake, and semantic models.
• Experience establishing enterprise data governance programs, business glossaries, data stewardship models, certified datasets, and master data management practices.
• Experience integrating legal, financial, document management, human resources, client relationship management, or Microsoft 365 platforms; familiarity with iManage, Intapp, Elite 3E, Aderant, SharePoint Online, Teams, or Entra ID is preferred.
• Experience with integration tools and patterns such as Azure Data Factory, APIs, secure file transfer, Talend, Boomi, event-driven integration, or comparable platforms.
• Programming or scripting experience with Python, PowerShell, .NET, or C# for data processing, automation, and custom integrations.
• Experience with Microsoft Purview or related data cataloging, classification, lineage, quality, and governance capabilities.
• Experience supporting data controls in a law firm or another highly regulated, confidentiality-sensitive environment.
• Relevant Microsoft data, Azure, Fabric, Purview, or Power BI certifications.
• Demonstrated ability to troubleshoot data discrepancies across multiple systems, identify root causes, and coordinate durable remediation.
• Excellent written and verbal communication skills, including the ability to explain technical concepts to non-technical audiences and handle confidential information with discretion.
• Availability for occasional after-hours support for major releases or production incidents.

Why Join Hinshaw?

At Hinshaw, we foster a collaborative and inclusive work culture. We offer competitive compensation, a comprehensive benefits program, and opportunities to work on enterprise-scale technology initiatives that directly support the firm’s strategic objectives. The Lead Data Engineer will play a critical role in shaping the firm’s data foundation and enabling analytics, reporting, and innovation across the organization.

Equal Opportunity Statement
We are committed to fostering an inclusive workplace and providing equal employment opportunities to all applicants and employees. Employment decisions are based on business needs, job requirements, and individual qualifications

As an EEO/AA employer, Hinshaw & Culbertson LLP will not discriminate in its employment practices due to an applicant’s age, race, color, religion, sex, sexual orientation, gender, gender identity, gender expression, national origin, protected veteran or disability status or any other factor prohibited by law.