Senior Data Engineer

Sovos is a global leader in tax compliance technology, transforming how businesses navigate changing regulatory requirements. The Senior Data Engineer will own the full data pipeline stack, including ingestion, transformation, modeling, reliability, testing, governance, and documentation, while partnering with business stakeholders to deliver trusted data outputs.

Responsibilities:

• Design and build the data models that power business-critical domains – today our financial data chain (Billing → ARR → Deferred Revenue → Rev Rec → Close Pack), tomorrow whatever the business needs most
• Deliver ingestion and transformation models with full test coverage and documentation
• Own reliability for vital pipelines – monitor, alert on, and recover from failures, and act as the escalation point when something breaks
• Establish data quality standards, testing conventions, and governance patterns for the broader team
• Treat documentation as a production deliverable – clear table descriptions, column definitions, and grain statements directly determine whether business users and AI consumers can trust the answer
• Partner directly with business stakeholders to make sure technical outputs actually map to what they need
• Pick up new domains and new tooling quickly, with minimal hand-holding – both the stack and the business context will evolve

Requirements:

• Advanced SQL – CTEs, window functions, LATERAL joins, performance tuning, and complex multi-source queries. This is the one non-negotiable
• Very strong communication skills – you adapt how you explain things to your audience, whether that's a finance analyst, an executive, or another engineer. You can describe a technical problem in plain language without jargon, walk a non-technical stakeholder through what went wrong and what happens next, and write documentation a business user can actually use to validate their own numbers
• 5–8 years in data engineering, with a track record of building and maintaining production pipelines and data models at scale
• A modern cloud warehouse or lakehouse – Snowflake, Databricks, or equivalent. You've owned the environment (roles, compute, governance, access controls), not just queried it
• A data modeling and transformation framework – dbt, Databricks/DLT, or equivalent. Modular models, tests, and documentation as standard practice
• Ownership of vital pipelines – keeping them running with no to very small downtime, and achieving high reliability through extensive testing and data baselines
• Data governance practices – PII handling, access control, and GDPR basics
• Cloud infrastructure and CI/CD fundamentals – Azure or AWS
• High adaptability – you learn unfamiliar business domains and new tooling autonomously and get productive fast
• Fluency in the revenue data chain: Billing → ARR → Deferred Revenue → Rev Rec → Close Pack
• B2B SaaS metrics: ARR, GRR, NRR, churn, and deferred revenue
• CRM/ERP integration (NetSuite, Salesforce, or similar): ELT patterns, API pagination, incremental loads
• Python for pipeline scripting and orchestration
• Model Context Protocol (MCP) or similar frameworks for connecting AI agents to structured data
• RAG architecture, hybrid retrieval strategies, or prompt engineering
• Domain knowledge in compliance, tax technology, or e-invoicing