Senior Data Engineer

Current is a growing accounting platform that partners with accounting and advisory firms across the United States. The Senior Data Engineer will strengthen a scalable data platform by developing dbt models, reconciling financial and operational data, building reliable data ingestion pipelines, and improving data quality, security, and production reliability.

Responsibilities:

• Build and maintain tested, documented dbt models across bronze, silver, and gold layers, with particular emphasis on reusable business models and consistent metric definitions
• Refine canonical models for firms, clients, employees, projects, time, work in progress (WIP), invoices, cash receipts, and revenue
• Reconcile financial and operational data to source systems. Investigate discrepancies and build controls that make data quality measurable and exceptions actionable
• Create entity crosswalks and historical logic that preserve reporting continuity through acquisitions, firm migrations, and changes in source systems
• Build and improve ingestion from SQL Server, cloud databases, SaaS platforms, and APIs using Python and SQL. Design incremental, idempotent loads that protect source performance and support reliable backfills
• Strengthen production reliability through automated tests, freshness checks, schema-change monitoring, alerts, logging, and clear operational documentation
• Partner with engineering colleagues to implement secure access and firm- and user-level data isolation and improve the performance and cost efficiency of Databricks and dbt workloads
• Work with Operations, Finance, HR, and Engineering to translate business requirements into reusable data products and stable, documented data contracts
• Contribute to Git-based development, CI/CD, code reviews, and shared engineering standards
• Understand the existing platform and align on near-term priorities for core models, reconciliation gaps, and the data platform roadmap (today’s state and where we want to take it)
• Deliver priority dbt models with clear definitions, tests, documentation, and traceability to source data, supporting a current firm or finance need
• Establish repeatable reconciliation checks and exception reporting for priority financial data, reducing manual investigation and increasing confidence in downstream reporting

Requirements:

• 5+ years in data engineering, analytics engineering, or a related role
• We also welcome candidates with equivalent depth of relevant experience
• Advanced SQL and strong production data-modeling skills, including dimensional models, historical data, and reusable business definitions
• Deep production experience with dbt, including testing, documentation, macros, dependencies, and deployment workflows
• The ability to reconcile financial and operational data, investigate differences between systems, and explain the causes clearly to technical and business audiences
• Strong Python skills and experience building ingestion or automation against APIs, databases, and external systems
• Practical understanding of incremental loading, change capture, idempotency, backfills, and schema evolution
• Experience with Git, code review, CI/CD, data-quality monitoring, and supporting production data pipelines
• Working knowledge of least-privilege access, sensitive-data handling, and secrets management, plus a willingness to learn Databricks
• Clear written and verbal communication, strong ownership, and sound judgment about when to build a reusable solution and when to keep it simple
• Databricks, Delta Lake, Unity Catalog, SQL warehouses, or Databricks Jobs and Workflows
• Prior Databricks experience is helpful; you can learn the platform in this role
• Azure, Azure Blob Storage, Windows-hosted ingestion, or on-premises SQL Server environments
• Accounting or professional-services data, especially billing, WIP, revenue, or financial reconciliation; systems such as Aiwyn, Practice CS, CCH Axcess, NetSuite, or UKG
• Multi-firm or multi-tenant data platforms, row-level security, or migrations that require entity mapping and historical reporting continuity