Senior Analytics Engineer, Commercial

Optro provides an AI-powered GRC Intelligence Platform that helps enterprises manage audit, risk, ESG, and information security. The Senior Analytics Engineer will build and own commercial data models, semantic layers, governance, documentation, and AI-powered automations that enable trusted revenue analytics and data-backed growth decisions.

Responsibilities:

• Own the function's roadmap and deliver on it
• Build tested, documented dbt models that match how the business defines ARR, bookings, and pipeline
• Build analytical models for upsell, whitespace, ICP fit, and account scoring
• Build semantic layers that make self-serve easy for analysts and business users
• Build AI-powered tools and automations that make the team faster
• Set the standard for data architecture, QA, and engineering practices (version control, code review, CI/CD, monitoring)
• Own the governance behind our data — clear rules for ownership, access, naming, lineage, and change, grounded in solid data governance practices
• Keep documentation current, including how to report on core metrics so every team gets the same answer
• Partner directly with stakeholders across Sales, Marketing, and Finance to define problems and scope the right solution
• Work with the enterprise data team (hub) and spoke analytics teams on data modeling initiatives. The hub owns ingestion and core infrastructure; you own the commercial modeling layer built on top
• Anticipate future analytics needs and proactively invest in them
• Represent the team in technical planning conversations
• Evaluate and recommend improvements to our data tooling and infrastructure
• Level up analyst’s dbt and modeling skills
• Help build a culture where good data and good decisions go hand in hand

Requirements:

• 8+ years in data or analytics roles, with 3+ in analytics engineering
• A leader's instinct for ambiguity — you set your own direction, know what good looks like, and build it from scratch
• Experience building analytical models for GTM teams, such as account scoring, ICP, propensity, or whitespace analysis
• Deep, hands-on expertise in data modeling — from raw source data to models people actually want to build on
• End-to-end ownership of production data models in Snowflake (or a similar enterprise warehouse), including the trade-off calls between completeness, performance, and speed
• Expert-level SQL and solid Python, both second nature for ad-hoc discovery, data workflows, and automation
• Fluency with our stack — Fivetran, dbt, and Snowflake — and experience building semantic layers, whether in dbt or Snowflake's semantic tables
• Experience inheriting, maintaining, and improving data models and ETL/ELT pipelines built by other people, adapting comfortably to different styles and existing constraints
• Experience applying software engineering practices to analytics: Git, code review, CI/CD, and automated testing
• Genuine curiosity about the business behind the data — you'll pick up our booking definitions, sales motions, and product portfolio quickly, because the right model starts with the right question
• The instinct to break big projects into clean, well-scoped pieces, while juggling conflicting priorities
• Excellent communication — you can translate a business question into a data model without losing anyone along the way
• ideally at a fast-paced, data-driven company