Senior Data Engineer

Womble Bond Dickinson (US) LLP is seeking a Senior Data Engineer to own and operate its enterprise data platform as it transitions from SQL Server to Microsoft Fabric. The role includes platform migration and reliability, financial data reconciliation, secure data engineering, reporting support, and leading data work for changes to core business systems.

Responsibilities:

• Operate the Firm’s Microsoft Fabric data platform day to day: monitor nightly orchestration and refreshes, triage and resolve failures, restart processing from the point of failure, track capacity use and cost, confirm data is complete before business users rely on it, and keep business owners informed when issues arise
• Audit and reconcile financial data throughout development: trace accounting transaction flows (time entry, billing, cash receipts, and general ledger postings) from source systems through the legacy warehouse and Fabric; build and run reconciliation and data quality checks (row counts, key coverage, and financial totals); investigate, explain, and document inconsistencies; and protect exact-output requirements for compensation and financial close reporting
• Work alongside the Firm’s implementation partner through the remaining migration phases: pair on converting SQL Server stored-procedure logic to Fabric notebooks (PySpark and Spark SQL), review converted logic for fidelity to existing business rules, and take ownership of each component as knowledge transfer is completed
• Design, build, and maintain ingestion and transformation pipelines across the bronze, silver, and gold layers, including database mirroring and metadata-driven copy pipelines from on-premises SQL Server, slowly changing dimensions, and incremental loading for the largest fact tables once migrated outputs are reconciled
• Implement and maintain data-level security, including Microsoft Entra group-based workspace and item permissions, OneLake security, row- and object-level security, column-level security and data masking, and Purview sensitivity labels, so that compensation, payroll, HR, personal, and client billing data is available only to authorized users, whatever tool they use to reach it
• Maintain Direct Lake semantic models with the BI team (relationships, DAX measures, calculation groups, security roles, and Excel connectivity) and support the move of reporting from SSAS and SSRS to Power BI
• Apply disciplined engineering practices to all data platform code: Git source control and peer review in Azure DevOps, CI/CD promotion from development to test to production, automated testing, secrets management, and current documentation of data models, source-to-target mappings, dependencies, and runbooks
• Prepare curated, documented, and secured data and semantic models for analytics and AI-assisted access (for example, Microsoft Copilot and Fabric data agents), and support the Firm’s AI and automation initiatives with governed access to that data
• Support the legacy SQL Server environment (staging and warehouse databases, SQL Agent jobs, replication, SSAS tabular models, and SSRS reports) while it remains in service; log and assess changes for their effect on the Fabric platform; re-point downstream feeds; and retire components once their replacements are signed off
• Maintain data feeds from the Firm’s business systems (financial management, CRM, HR and payroll, and others) and lead the data engineering work for planned changes to core business systems, including data mapping, conversion validation, and continuity of historical reporting

Requirements:

• Bachelor's degree in computer science, information systems, engineering, mathematics, accounting, or a related field, or equivalent practical experience
• 5+ years of hands-on data engineering or data warehouse development, including production ownership of ETL/ELT pipelines and a dimensional (Kimball) data warehouse
• Advanced T-SQL and SQL Server skills: reading and refactoring complex stored procedures, query and index tuning, SQL Agent, and working knowledge of replication and change data capture
• Hands-on experience with Microsoft Fabric (lakehouses and OneLake, data pipelines, notebooks, and mirroring or comparable ingestion) and medallion architecture; strong Azure Databricks or Synapse Spark experience will be considered
• Proficiency in PySpark and Spark SQL, including Delta tables
• Working knowledge of accounting, preferably cash-basis accounting in a professional services environment
• Ability to follow accounting transaction flows (for example, from time entry through billing, cash receipts, and general ledger posting) and to audit and reconcile financial inconsistencies during development, resolving variances with business owners
• Practical experience protecting sensitive data through role-based access, row- and object-level security, and least-privilege design
• Working knowledge of Power BI semantic models and DAX
• Experience with Git and Azure DevOps (or GitHub), including branching, pull requests, and CI/CD deployment across environments
• Clear written and verbal communication, including the ability to explain technical tradeoffs to finance and practice leaders and to write documentation others can follow
• Discretion with confidential client, financial, and personnel information, and the ability to work independently and own outcomes
• This position is subject to export control laws
• Law firm or professional services experience, including familiarity with time and billing, WIP, accounts receivable, collections, realization, and timekeeper credit
• Experience with Aderant, Elite 3E, or other legal financial management systems, and with CRM (Salesforce or Microsoft Dynamics 365) and HR or payroll data
• Experience migrating SSAS tabular models and SSRS reports to Power BI, Direct Lake semantic models, and paginated reports
• Experience with OneLake security, Microsoft Entra ID, Microsoft Purview (sensitivity labels and lineage), and Fabric capacity administration
• Experience with Fabric deployment tooling (for example, fabric-CI/CD or deployment pipelines), TMDL, and Python testing frameworks
• Experience supporting a financial system implementation or large data conversion, including API-based ingestion from cloud applications
• Regular use of AI-assisted development tools (for example, GitHub Copilot or Claude Code) with disciplined code review and data handling