Data Engineering Manager

Husch Blackwell LLP is a full-service litigation and business law firm serving clients with domestic and international operations. The Data Engineering Manager will lead the design and development of the firm’s cloud-based data platform, establish data engineering standards, and ensure reliable, secure, and accessible data for analytics, reporting, applications, and AI. The role also supervises data engineering staff, manages initiatives and vendors, and partners with technical and business stakeholders.

Responsibilities:

• Supervising all Data Engineering staff persons
• Foster professional growth and skill development in their direct reports
• Delegate tasks and responsibilities effectively, ensuring optimal workload distribution and project efficiency
• Conduct regular performance evaluations, provide constructive feedback, and set clear goals for direct reports
• Promote team engagement through regular communication, recognition, and a collaborative, inclusive environment
• Identify training and development opportunities to keep team capabilities current with modern data engineering practices and cloud technologies
• Provide technical and architectural leadership for the firm’s data platform, with a primary focus on building and operating modern, cloud based data foundations
• Define and promote best practices for data engineering across the firm, including standards for code quality, testing, deployment, monitoring, and documentation
• Design, implement, and maintain reliable processes for acquiring, consolidating, and organizing data from core systems and external sources, and making it available for downstream use
• Ensure that data engineering solutions are scalable, maintainable, and reliable, including management of performance, availability, and capacity risks
• Partner with Data Science & AI, Information Design & Engineering, IT Operations, and business leaders to understand challenges and translate them into data requirements and platform improvements
• Contribute to data and AI governance by implementing and enforcing controls for data quality, lineage, access, and responsible use within the data platform
• Lead the planning, deployment, and ongoing management of data engineering initiatives and related projects
• Evaluate and prioritize data engineering work based on firm needs, strategic value, and available capacity
• Manage and document projects, including scope, timelines, risks, dependencies, and key decisions
• Establish and maintain effective relationships with key technology vendors and service providers that support the data platform

Requirements:

• Bachelor's degree in computer science, engineering, information systems, or related field, or equivalent industry experience
• At least 3–5 years of experience leading data engineering or closely related technical teams, including responsibility for setting direction, standards, and priorities
• Experience managing budgets and making cost conscious decisions about tools, platforms, and services
• Strong understanding of modern data engineering practices, including data ingestion, consolidation, transformation, and organization to support analytics and AI
• Extensive experience with data management and data transformation, including performance, reliability, and scalability considerations
• Advanced SQL experience and strong understanding of how to design and optimize data structures in relational and other data storage technologies
• Experience designing and managing data solutions in modern cloud environments (for example, Microsoft Azure or Amazon Web Services), including use of platform services
• Working knowledge of Python and common data tooling, with sufficient depth to review designs and solutions produced by engineers and to engage effectively with Data Science & AI teams
• Demonstrated experience collaborating with data scientists, analysts, and AI practitioners, and understanding how engineering choices affect downstream analytics and AI work
• Broad familiarity with data visualization, reporting, and application needs so that data platforms are designed with end to end use in mind, even when this role does not own the final experiences
• Extensive experience with software development life cycle and software engineering best practices, including version control, testing, deployment, monitoring, and secure handling of data
• Ability to define and implement data and platform standards, and to guide teams in adopting consistent, high quality engineering practices
• graduate degree preferred