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, evidence, findings and organizations. Much of it is still locked in systems built to run workflows. We want to turn it into a trusted, scalable product asset.
You'll design the data models, metrics, semantic layer and dashboards that make this data ready for analytics, product features, and AI/ML. You’ll also help us measure what we ship, so product decisions rest on evidence rather than opinion.
This is not a reporting role or a ticket queue. You'll work hand in hand with our Data Engineer to build a modern data platform that becomes part of the product itself. It will connect legacy and modern systems as Intact transforms domain by domain. You'll work closely with me and with our Product, Engineering, and Applied AI/ML teams.
Sounds exciting? We'd love to hear from you.
What you will own
Data at Intact sits close to Product and Operations. Understanding how certification bodies use our platform takes real domain context, as audit and certification processes are complex and raw data is easy to misinterpret.
You will own the path from raw data to trusted decisions. Working alongside our Data Engineer, you will sit between the business and the platform, combining analytics engineering, product analytics and close stakeholder partnership.
We are starting fresh. There is no warehouse or metric catalogue yet, so you will shape how data is modeled, defined and used at Intact from the beginning.
Understand what the business needs. Work with Product, Customer Success, Operations and leadership to learn which decisions they make and which questions they cannot answer today. Turn this into prioritized use cases, metric definitions and source requirements that decide what gets built first.
Partner with the Data Engineer on the foundations. Bring the analytics perspective to stack choices, source prioritization, ingestion and warehouse design. Own the conventions for the transformation layer, project structure, naming, testing and documentation.
Model product usage. Learn how customers use Intact Platform across audit planning, auditor management, certification decisions and customer portals. Turn raw application data into clean models for activation, adoption, feature usage, retention and customer health.
Model internal operations. Build reliable data on support tickets, implementation and PMO projects, onboarding timelines and delivery efficiency, so operational teams can see where time and effort go.
Make metrics trustworthy. Define core metrics with stakeholders, document what each number means and how it is calculated, and resolve conflicting definitions before they spread.
Run data quality as a product. Together with the Data Engineer, put tests, freshness checks, monitoring and clear ownership on every important model.
Improve product instrumentation. Find blind spots in the data we collect and work with engineers to design events and source data that make future analysis reliable.
Partner with Product Managers to design A/B tests, staged rollouts and feature-flag experiments. Define hypotheses, success and guardrail metrics, sample sizes and analysis plans, then read the results honestly. Help teams judge whether releases and AI-powered capabilities create the intended customer and business outcomes.
Enable self-service. Build marts, a semantic layer, documentation and dashboards that let teams answer recurring questions without a custom query each time.
Raise data literacy. Run hands-on sessions and write documentation that helps colleagues read, question and use data well.
What makes this role different
You won't only build dashboards, and you won't only maintain pipelines. You'll connect what certification bodies do in our platform to the numbers our teams rely on.
The SQL is the easy part. Much of the challenge is understanding how audits, certificates and findings move through Intact Platform, and how customers configure it to fit their own processes. When a number looks off, you'll want to find out why.
As one of the first members of our data team, you'll help decide how we model, test and document data, and how data work fits into the rest of Intact.
What success looks like in your first months
You know our key stakeholders, their top questions and the decisions they need data for.
A prioritized list of first use cases, with agreed metric definitions and required data sources, guides what gets built.
Together with the Data Engineer, you have shaped the target architecture and the first sources to ingest.
Modeling conventions, testing standards and documentation templates are written down and used for the first models.
The first trusted models and dashboards for product usage and operations are live and used by Product and Customer Success.
Teams start using the same definitions for the most important metrics, and gaps in product instrumentation are known.
What we are looking for
5+ years in data analytics (e.g. Data Analyst, Product Analyst or BI Analyst), with a growing focus on data modeling and analytics engineering.
You have built reusable analytical models and datasets yourself, not only one-off analyses.
Advanced SQL and solid Python for analysis and automation, with experience optimizing queries on large datasets in a cloud data warehouse (e.g. Snowflake, BigQuery, Databricks, ClickHouse).
Hands-on experience with dbt or a similar transformation framework. Exposure to orchestration tools such as Airflow or Dagster is a plus.
Solid grasp of data modeling, dimensional modeling, event data, slowly changing dimensions and metric design.
Experience with at least one BI or semantic layer tool (e.g. Looker, Power BI, Metabase, Tableau).
Engineering habits - Git, code review, testing, CI/CD and documentation.
Strong product and business sense, with experience gathering requirements from non-technical stakeholders. You can turn a vague question into a clear definition, a dependable model and an explanation people act on.
Experience designing and analyzing A/B tests or staged rollouts, with a sound grasp of statistical significance, power and common pitfalls. You know the difference between correlation and causation.
Clear communication in English with technical and non-technical colleagues.
Nice to have
Experience in B2B SaaS, ideally with product analytics or customer health scoring.
Familiarity with event tracking design (e.g. Segment, Snowplow, PostHog).
Exposure to compliance, audit or certification domains.
German language skills.
WHY US?
A platform to shape.
As one of the first analytics engineers in the Data and AI team, you'll help shape the platform and its standards from the start.
Work with purpose.
Your work helps safeguard quality, safety, and sustainability worldwide.
An international team.
You'll work closely with colleagues across Product, Engineering, and operations around the world.
Flexible work.
Flexible work models, with a 100% remote option.
Lebring office perks.
Food vouchers, free parking, and easy access to public transport.
Room to grow.
We don't just allow personal development, we encourage it. Your growth is our gain.
Fair compensation.
From €75,000 gross per year (full-time), with higher pay depending on seniority and experience.
Start:
As soon as possible.