Sr. Data Engineer

Goodwin•Washington, DC

About The Position

Join Goodwin’s Global Operations Team, and make a real impact on a global scale. At Goodwin, we work with some of the world’s most successful and innovative investors, entrepreneurs and disruptors in the life sciences, private equity, real estate, technology and financial industries, and where they converge. As part of the Global Operations Team – all business professionals at the firm – you’ll collaborate with colleagues from varied backgrounds and experiences, fostering an environment where cross-functional learning, networking, and collaboration are at the core of what we do. Here, we’re not just supporting a law firm; we’re partnering with attorneys and clients to deliver cutting-edge solutions in high-stakes litigation and dispute resolution, world-class regulatory compliance and advisory services, and complex transactions. Our commitment to integrity, ingenuity, agility, and ambition drives us, and we’re proud to have been recognised as the “Best Business Team” by The American Lawyer. This is your opportunity to grow professionally in a dynamic, global environment, surrounded by forward-thinking peers. The role of Senior Data Engineer is essential for the successful modernization of our data platforms and practices. This role will play a crucial role in as a point of contact for the Finance Data and Analytics Team, the Senior Data Engineer will collaborate with IT teams, business units, and senior management to drive change and foster a data-driven culture. They will provide expert guidance, informed research, and support, ensuring successful implementation and stakeholder engagement throughout the lifecycle of data projects.

Requirements

Bachelor’s degree in Computer Science or a related field is preferred.
Five or more years of experience in a senior data engineering or database architecture role.
Experience working with cloud-based data platforms and services, such as Snowflake or Microsoft Fabric.
Advanced SQL development and modeling skills, including associated tools.
Strong experience designing, building, and maintaining data integration and ETL/ELT pipelines to support creation of data warehouse and analytical data models.
Working knowledge of Python for data engineering and automation.
Proven experience with relational database management systems and related data technologies.
Experience integrating with APIs and working with structured and semi-structured data formats such as (e.g. JSON and XML).
Experience using Git or similar tools for source control and version management.
Experience working in an Agile development environment.
Experience with database performance monitoring and tuning.
Familiarity with AI and large language model technologies.
Ability to work independently and collaborate effectively in team environments
Excellent organizational, customer service, and problem-solving skills.

Nice To Haves

Industry-recognized database or cloud platform certifications are a plus.
Experience with common law firm application databases such as Aderant Expert, Foundation, iManage.
Experience with Finance, ERP, and Financial Reporting.
Experience with third-party database monitoring and diagnostics tools.

Responsibilities

Manage, evaluate, and enhance our existing finance-related data platforms, identifying opportunities to leverage emerging technologies and cloud-based solutions.
Contribute to the establishment of standards and procedures for data governance, integration, quality management, and lifecycle management.
Evaluate, enhance, and modernize the Firm’s data platforms, including exploring emerging technologies and cloud-based solutions to improve scalability, accessibility, and security.
Contribute to establishing and optimizing standardized procedures for data management, data governance, data integration, quality management, and lifecycle management to ensure data consistency, accuracy, and timeliness.
Collaborate with cross-functional teams, business units, and senior management to drive data-driven initiatives, foster a data-driven culture, and ensure alignment with organizational goals.
Monitor and optimize enterprise data platforms and data engineering processes, identify areas for improvement, and drive innovation to enhance data processing efficiency, data quality, and overall system performance.