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Sample document — fictional candidate for illustration purposes only. Your CV is prepared from your own record.

Eva Müller
VP Data & Head of Data Platform
eva.mueller@email.com +49 176 1234 5678 Berlin, Germany linkedin.com/in/evamüller-data
Certifications & Executive Education
Executive Program in Data Strategy — Stanford Graduate School of Business, 2022 Databricks Certified Associate Developer — Databricks, 2021 dbt Certified Analytics Engineer — dbt Labs, 2023
Professional Summary

Senior data executive with 11 years of experience building data platforms and analytics organisations at scale. Joined N26 as its first dedicated data hire; scaled the function to 28 people across data engineering, analytics, and data science. Combines deep technical fluency (dbt, Databricks, Tableau) with board-level communication, product roadmap ownership, and regulatory compliance experience in the European fintech sector.

Platform & Organisational Highlights
200+ Platform users scaled to (from 0)
78% Reduction in data incident rate
28 Data team headcount built
€2.1M Annual infra savings via platform consolidation
Technology Stack
dbt Databricks Tableau Python SQL Spark Snowflake Airflow Fivetran Terraform AWS Monte Carlo (data observability) Git
Experience
VP Data & Head of Data Platform Jan 2021 – Present
N26, Berlin
  • Business question: How could N26 reduce the data incident rate that was eroding analyst trust in production dashboards? Approach: Deployed Monte Carlo data observability across 1,400 dbt models; established SLA tiers, on-call rota, and incident post-mortem culture. Outcome: Incident rate fell 78% within 9 months; analyst NPS for the data platform rose from 32 to 61.
  • Business question: Was maintaining three separate BI tools (Tableau, Looker, Metabase) sustainable at scale? Approach: Led a platform consolidation project, migrating to a single Tableau + dbt semantic layer; ran change management with 9 product squads. Outcome: Consolidated to one platform saving €2.1M/year in licensing and reducing analyst onboarding time from 3 weeks to 5 days.
  • Business question: How should the company demonstrate GDPR-compliant data lineage to BaFin regulators? Approach: Built end-to-end data lineage documentation using dbt's native graph and a custom metadata store; produced an audit pack reviewed by external counsel. Outcome: Passed BaFin data governance review with zero findings; process now used as a template for N26's expansion markets.
  • Scaled data team from 9 to 28 across 4 sub-teams; introduced levelling framework and data career ladder used across 3 European offices.
Director of Data Engineering Mar 2018 – Dec 2020
N26, Berlin
  • Business question: Could the data platform support 200 concurrent users from 3 users when N26 scaled from seed to Series D? Approach: Migrated from a single PostgreSQL instance to a Databricks + Snowflake architecture; introduced dbt for transformations and Fivetran for 22 source connectors. Outcome: Platform scaled to 200 active users with no degradation in query performance.
  • Business question: Were manual data extractions creating compliance exposure for the finance team? Approach: Designed a governed data access layer using Snowflake row-level security; replaced 14 manual extraction scripts with monitored pipelines. Outcome: Eliminated all uncontrolled extractions; internal audit closed 6 open data-access findings in the following cycle.
  • Hired and developed the first 9 data engineers at N26; established engineering interview process and technical onboarding programme.
Senior Data Engineer → Lead Data Engineer Jun 2015 – Feb 2018
Zalando, Berlin
  • Business question: Could a unified customer 360 view be built from 7 disparate source systems to power personalisation? Approach: Designed a Spark-based data vault on AWS EMR integrating CRM, web, app, and logistics data; modelled 28 curated entities. Outcome: Customer 360 powered a personalisation engine that increased email campaign click-through by 19% across 14M active customers.
  • Led migration of 80 legacy Informatica jobs to Python + Spark; delivered on time and 15% under budget.
Data Analyst Sep 2013 – May 2015
Deutsche Telekom, Bonn
  • Built churn prediction models for residential broadband; provided monthly scoring to the retention marketing team.
  • Outcome: Retention campaign powered by model scores achieved 24% lower churn rate vs. control group in 6-month trial.
Additional Skills
  • Data governance & GDPR compliance
  • C-suite & board reporting
  • P&L responsibility & vendor management
  • Data mesh & federated architecture
  • Executive hiring & team building
  • German (native), English (C2), French (B2)
Education
Executive Program in Data Strategy 2022
Stanford Graduate School of Business
MSc Information Systems Oct 2011 – Sep 2013
Humboldt-Universität zu Berlin — Summa Cum Laude, Thesis on Real-Time Stream Processing
BSc Business Informatics Oct 2008 – Jul 2011
Humboldt-Universität zu Berlin — Magna Cum Laude