Senior Analytics Engineer

About the role

The ideal candidate thrives in an innovative, fast-paced environment and is collaborative, accountable, ready, and empathetic. We're looking for individuals who believe they can accomplish more as a team and create lasting growth for themselves and others.

Responsibilities

• Design, build, and maintain dbt data models (Staging, Intermediate, Mart layers) following SolarWinds modelling standards, with a focus on consumption-ready, well-tested data products
• Deliver high-quality Tableau dashboards and self-serve analytics assets for business teams across Revenue, Customer Success, Finance, and Product — from requirements through to stakeholder sign-off
• Own the Tableau-to-dbt integration layer: consume MetricFlow semantic layer definitions in Tableau Cloud, ensuring metrics are consistent, governed, and aligned with company-wide standards
• Partner directly with stakeholders across assigned business domains to translate ambiguous business problems into scalable analytical solutions, proactively identifying gaps in requirements, aligning stakeholders, and driving clarity on metrics, logic and desired outcomes
• Champion data quality — write dbt tests, enforce delivery SLAs, and embed lineage documentation as standard practice
• Lead and run CI/CD processes for your dbt models — branch-based development, PR reviews, and automated test gates
• Support and mentor junior and mid-level analysts, promoting best practices in SQL, dbt modelling, and Tableau development
• Contribute to the rollout of self-serve analytics enablement programmes for business users, reducing dependency on direct analyst requests

Requirements

• 5+ years of experience in analytics engineering, business intelligence, or a related data role
• Production-grade dbt experience (dbt Core or dbt Cloud)
• Advanced Tableau expertise – including development or performant, governed and user-friendly analytical solutions integrated with modern semantic layer & with the ability to clearly explain modelling, metric and design decisions with stakeholders
• Python proficiency for analytics use cases — data wrangling, scripting, custom dbt macros, or exploratory analysis
• Strong SQL fundamentals — complex analytical queries, window functions, CTEs, aggregations, and query performance awareness
• Experience with a modern cloud data warehouse (BigQuery, Snowflake, or Redshift) including understanding of materialisation and performance trade-offs
• Git and branch-based development workflows — you treat analytics code with software engineering discipline
• Ci/CD experience in a data context — dbt test gates, Pr-triggered runs, or equivalent pipeline automation
• Solid understanding of data modelling concepts — star schema, slowly changing dimensions, metric normalisation
• Strong communication skills — you can present data findings to non-technical audiences and translate vague business questions into clear model briefs

Skills

• Hands-on MetricFlow or dbt Semantic Layer experience in a production environment
• Familiarity with the Tableau + dbt Cloud connector (Tableau 2025.2+) for semantic layer consumption
• Familiarity with Salesforce or NetSuite as source systems
• Experience in a SaaS or recurring revenue business — ARR, churn, expansion, and NRR metrics
• Exposure to Dataplex, Fivetran, or similar data cataloguing and ELT tooling
• Experience with next-generation self-serve BI platforms (Omni, Looker, ThoughtSpot) — we are actively evaluating alongside Tableau
• Working knowledge of GDPR data residency requirements in an analytics context
• Comfort using AI coding assistants and LLM-based tools in analytics workflows