Analytics Engineer

About the Role 

We are looking for an Analytics Engineer who combines deep business understanding with analytical expertise to shape the internal products we build. As AI-powered analytics enables business teams to create their own reports and dashboards, this role focuses on defining and governing trusted metrics, working with data teams to ensure their reliability, and identifying opportunities to improve business workflows. Embedded within a product team, you will translate those insights into requirements and help develop data and analytical features that support better decisions and actions within the product itself. 

You will be assigned to Sales & Marketing domain and work alongside the product pod that serves it, with the data platform and modern AI tooling to build on. 

Key Responsibilities 

• Metric ownership: Own the definitions, documentation, and change control for your domain’s certified metrics. Maintain a single source of truth so that teams asking the same question get the same number, and act as the point of resolution when definitions are challenged or diverge. 
• Data quality: Define and enforce freshness, completeness, and accuracy expectations for the datasets and pipelines behind your domain’s metrics. Build automated dbt tests and monitoring into the pipelines you own, investigate failures and anomalies, trace root cause back to source, and escalate upstream issues to the Data Platform team with supporting evidence. 
• Analytics engineering (dbt): Design, build, and maintain the dbt models and semantic layer that serve your domain — written in SQL and Python, version-controlled in Git, tested, and documented. Follow the team’s modelling standards for layering, naming, and incremental logic, and keep dbt documentation and lineage current as models change. 
• In-product features: Build in-product data and insight features end to end: embedded metric panels and insight cards, exception and alert surfaces, prioritized work queues, agent-assisted workflow steps, and the instrumentation behind them. Prototype in a sandbox, then ship through engineering review, tests, and feature flags alongside the pod’s engineers. 
• Analysis & reporting: provide support to business users with more complex analytics requests, with the goal to enable them to self-service in the long run. 
• Collaboration: Partner with product managers, engineers, and business owners in your domain; document your work so others can use and extend it; and support business users querying certified metrics themselves. 

Required Skills & Qualifications 

• SQL (required): Strong query writing, data modeling, and performance-aware analytics against large datasets; comfortable with CTEs, window functions, and reading an execution plan when a query is slow. 
• Demonstrated ability to apply AI to data and product analysis, including using AI-assisted tools to investigate business questions, building and validating analytical outputs such as dashboards, reports, or prototypes, and developing semantic models and metric definitions that support reliable AI-powered analytics. 
• Python (required): Proficient in data analysis, transformation and automation scripting, and rapid prototyping (pandas or similar, working against APIs, testing your own code). 
• Experience: 3-5 years in analytics engineering, data analysis, BI, or a comparable data role, with at least one example of work you owned end to end. 
• Data quality instinct: Habit of validating your own numbers before publishing them — reconciliation against source, sanity checks, and a documented definition behind every metric you ship. 
• Business curiosity: Ability to learn a business process deeply and translate it into metrics, instrumentation, and requirements engineers can build against. 
• Communication: Clear written and verbal communication with business stakeholders; able to explain a number, defend a definition under challenge, and say when the data does not support the conclusion. 

Nice to Have 

• Experience with modern BI or AI-native analytics platforms and metric certification workflows. 
• Advanced dbt practice: custom generic tests, macros and packages, exposures, or CI-enforced model builds. 
• Data quality or observability tooling beyond dbt tests (Great Expectations, Elementary, or similar). 
• Hands-on use of agentic AI development tooling to prototype features or automate analysis. 
• Exposure to shipping software under engineering standards: version control, code review, tests, feature flags.