> Markdown version of [/jobs/ext/4041150-senior-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/4041150-senior-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - **Company:** Intact GmbH - **Location:** Lebring, Austria (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Airflow, Data Analysis, BigQuery, Software Documentation, Code Review, Continuous Integration, Dimensional Modeling, Python (Programming Language), Operational Data Store, Query Optimization, Power BI, Snowplow, SQL Databases, Tableau (Software), Sql Optimization, Snowflake, Git, Data Analytics, Vertica, Looker Analytics, Databricks - **Published:** October 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=477d20c19b0bff69 ## About the Role * 5+ years in data analytics (e.g. Data, Product or BI Analyst) with a growing focus on analytics engineering. You've built reusable models, not only one-off analyses. * Advanced SQL and solid Python, including query optimization in a cloud warehouse (e.g. Snowflake, BigQuery, Databricks, ClickHouse). * Hands-on dbt or similar. Airflow or Dagster is a plus. * Solid grasp of dimensional modeling, event data, SCDs and metric design. * Experience with a BI or semantic layer tool (e.g. Looker, Power BI, Metabase, Tableau). * Engineering habits: Git, code review, testing, CI/CD, documentation. * Experience designing and analyzing experiments, with a sound grasp of significance, power and common pitfalls. * Strong product sense and the ability to turn vague questions from non-technical stakeholders into clear definitions and dependable models. Clear communication in English. Nice to have: B2B SaaS and product analytics or customer health scoring, event tracking design (Segment, Snowplow, PostHog), compliance, audit or certification domains. ## Description Hi, I'm Nishya, Head of Data and AI at Intact - the world's leading software provider for the Testing, Inspection and Certification (TIC) industry. We're looking for a Senior Analytics Engineer to build the data foundation for the next generation of our product. We have years of operational data on audits, certifications, standards, findings and organizations, much of it still locked in systems built to run workflows. You'll turn it into a trusted product asset: data models, metrics, a semantic layer and dashboards that power analytics, product features and AI/ML. We're starting fresh. There is no warehouse or metric catalogue yet. Working hand in hand with our Data Engineer, and closely with me, Product, Engineering and Applied AI/ML, you'll shape how data is modeled, defined and used at Intact from day one. What you will own * Understand the business. Learn which decisions Product, Customer Success, Operations and leadership need to make, and turn that into prioritized use cases, metric definitions and source requirements. * Build the foundations. Bring the analytics perspective to stack, ingestion and warehouse design. Own the conventions for transformation, naming, testing and documentation. * Model product usage and operations. Build clean models for activation, adoption, retention and customer health, as well as for support, implementation projects and delivery efficiency. * Make metrics and data trustworthy. Define and document core metrics, resolve conflicting definitions, and put tests, freshness checks and clear ownership on every important model. * Close instrumentation gaps. Work with engineers to design events and source data that make future analysis reliable. * Measure what we ship. Partner with Product Managers on A/B tests, staged rollouts and feature-flag experiments, from hypotheses and guardrail metrics to an honest reading of the results. * Enable self-service. Build marts, a semantic layer, dashboards and documentation, and run hands-on sessions that help colleagues use data well. Why this role is different * The SQL is the easy part. The real challenge is understanding how audits, certificates and findings move through Intact Platform, and how customers configure it for their own processes. When a number looks off, you'll want to know why. Success in your first months * You know our key stakeholders and have agreed a prioritized list of first use cases, metric definitions and data sources. * Together with the Data Engineer, you've shaped the target architecture and the first ingestions. * Modeling, testing and documentation standards are written down and in use. * The first trusted models and dashboards for product usage and operations are used by Product and Customer Success.