Analytics Engineer / Data Analyst (m/f/d)
IT, Data Science
Innsbruck, Austria
EUR 3,954+ / month
We're looking for an Analytics Engineer / Data Analyst to join our Business Intelligence & Analytics team — based in Innsbruck or Vienna, or remote within the EU — with immediate effect.
This is a blended role. You'll spend most of your time on analytics engineering (building the modeled, tested, documented data layer) and data analytics (turning that data into insights and decisions), with a bit of hands-on data engineering to keep the pipelines healthy. If you like owning data end-to-end — from raw source to a dashboard a stakeholder actually trusts — and want real influence in a growing European FinTech, we'd love to hear from you. Apply now!
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Analytics engineering — the core of the role
- Design, build, and maintain our transformation layer in dbt on BigQuery — well-structured, tested, and documented data models that power analytics, data science, and self-service BI
- Own and evolve the semantic/BI layer in Looker (LookML): governed, reliable metrics and dashboards for teams across the company
- Safeguard data quality end-to-end — testing, documentation, and continuous improvement across the stack
Data analytics — the core of the role
- Analyze large datasets to surface trends, patterns, and anomalies that drive product and business decisions
- Define, implement, and monitor KPIs across business areas
- Support management with forecasts, scenario analyses, and ad-hoc deep dives
- Communicate and present insights and findings clearly and persuasively to stakeholders across the business
Measurement & product analytics — owning the full loop
- Partner with product and engineering teams to define and set up event tracking and instrumentation — deciding what to measure and making sure it lands cleanly and reliably in the data
- Translate product and business questions into clearly defined metrics, from the tracking plan all the way through to the data model
- Bring those metrics to life on dashboards that stakeholders genuinely use to make decisions
A bit of data engineering
- Help maintain and extend our ELT pipelines and orchestration (Fivetran, Dagster), keeping ingestion reliable and the warehouse performant
AI as a force multiplier
- Use AI tools to accelerate analysis and enable self-service analytics — e.g. Conversational Analytics in Looker or AI-assisted exploration
- Help build AI-powered internal tools and automations, such as agents for recurring analysis and reporting tasks
- Strong SQL is a must — you write complex queries with confidence
- Solid hands-on experience with dbt (data modeling, testing, documentation) — this is central to the role
- Experience with a cloud data warehouse; BigQuery preferred
- Experience with a BI / semantic layer; Looker/LookML preferred (Tableau or Power BI also welcome)
- Comfortable with Python for analysis, data manipulation, and automation
- Strong analytical thinking paired with a sharp sense of business impact
- Experience defining metrics and instrumenting event tracking together with engineering (tracking plans, event taxonomies) — you're comfortable owning measurement from definition through to the dashboard
- Proactive, self-directed working style and clear communication with technical and non-technical audiences alike
- Open and curious about AI tools in an analytics context — you use them actively to work more efficiently and assess their output critically
- Very good English (our working language); German is a plus
Nice to have
- Experience with data transformation/orchestration tooling (Dagster, Airflow, Prefect)
- Product analytics experience and tooling (e.g. Mixpanel, GA4, or similar)
- Familiarity with fintech / payments data
- Knowledge of machine learning, statistics, or advanced analytics
- First experience with AI-assisted analysis or agent frameworks
- A meaningful role in a growing European FinTech with real impact on product and strategy
- Gross monthly salary of min. EUR 3.954 (14x p.a.) – depending on experience, with readiness to overpay
- Flexible working hours and hybrid setup
- Flat hierarchies, international team, startup culture, and lots of ownership
- Team events and Workations
