Senior Data Engineer

Build your future with Sovos. If you’re seeking a career where innovation meets impact, you’ve come to the right place. As a global leader, Sovos transforms tax compliance from a business requirement to a growth engine while revolutionizing how businesses navigate the ever‑changing regulatory landscape.

At Sovos, we’re dedicated to more than just solving compliance challenges – we’re committed to making a positive and lasting difference. Our teams work at the modern edge of digital technology, solving complex business challenges while enriching our local communities. We provide purpose‑built systems that give you the tools to thrive in a world of increased visibility, faster reporting and greater control over business processes.

In this role, the Senior Data Engineer owns the full pipeline stack from ingestion through transformation to the finance‑trusted, close‑ready outputs relied on by FP&A, Accounting and leadership. You’ll design the Snowflake architecture and dbt model infrastructure for the U.S. topline data chain, ensuring reliable pipelines through close and driving business impact. You’ll also shape how AI consumers interact with structured financial data, treating them as first‑class stakeholders alongside finance teams.

• Design and build the Snowflake schema and dbt model architecture for the U.S. topline data chain:
  Billing → ARR → Deferred Revenue → Rev Rec → Close Pack.
• Deliver ingestion and transformation models with full test coverage and documentation across all assigned use cases.
• Own pipeline reliability through financial close – monitor, alert on failures and recover; serve as the primary on‑call escalation point.
• Establish data quality standards, dbt testing conventions and Snowflake governance patterns for the broader team.
• Partner with the Analytics Engineer on business‑layer model design to ensure outputs map accurately to finance requirements.
• Extend the dbt model layer to cover OPEX, COGS, commissions and ASC 340‑40 capitalized software as scope grows.
• Write dbt YAML documentation – table descriptions, column definitions and grain statements – as a first‑class production deliverable.
• Own row‑level security and model freshness SLAs for real‑time AI queries, treating AI consumers as first‑class stakeholders alongside finance.

• 5–8 years of data engineering experience with a track record of building and maintaining production pipelines and dbt projects at scale.
• Advanced SQL skills: CTEs, window functions, LATERAL joins, performance tuning and complex multi‑source financial queries.
• Production dbt experience: modular models, tests, documentation and semantic metadata for both AI and BI consumers.
• Snowflake proficiency: roles, warehouses, governance, masking policies and row‑level security – not just querying, but owning the environment.
• Experience supporting financial close cycles, including on‑call ownership of pipeline failures and an understanding of what W D 1 means and why it matters.
• Fluency in the revenue data chain (Billing → ARR → Deferred Revenue → Rev Rec → Close Pack) and ability to engage finance stakeholders directly on data discrepancies.
• Strong communication skills: able to explain technical trade‑offs to non‑technical audiences and write documentation usable by finance analysts.
• Preferred:
  Experience integrating CRM/ERP systems (Net Suite, Salesforce or similar) into a warehouse layer, including ELT patterns, API pagination and incremental loads.
• Preferred:
  Python for pipeline scripting and orchestration.
• Preferred:
  Cloud infrastructure experience – Azure (App Service, Azure AD, Functions) or AWS; familiarity with auth patterns (JWT, OAuth) and CI/CD fundamentals.
• Preferred:
  Experience with Model Context Protocol (MCP) or similar frameworks for connecting AI agents to structured data.
• Preferred:
  Background in B2B SaaS metrics – ARR, GRR, NRR, churn and deferred revenue.
• Preferred:
  Familiarity with data governance practices – PII handling, access control and GDPR basics.
• Nice to have: RAG architecture, hybrid retrieval strategies or prompt engineering experience.
• Nice to have: MLOps or ML pipeline experience.
• Nic…