Data Engineer and Analytics Engineer with several years of experience designing and delivering scalable data infrastructure, end-to-end pipelines, and business intelligence solutions across cloud platforms. Proven track …
Data Engineer and Analytics Engineer with several years of experience designing and delivering scalable data infrastructure, end-to-end pipelines, and business intelligence solutions across cloud platforms. Proven track record of taking data products from raw ingestion through to stakeholder-ready analytics, with a strong focus on data quality, performance, and business impact. Based in Berlin with permanent residency (Niederlassungserlaubnis); fluent in English and actively developing German.
<p>Designed and delivered a full-stack data and analytics platform covering both operational and analytical workloads end-to-end.<br>
Built the operational layer using Django and PostgreSQL hosted on Heroku, including a Next.js/React frontend, providing a robust backend for data collection and management.<br>
Implemented the analytical layer with dbt for ELT modeling and Power BI for stakeholder-facing dashboards, enabling data-driven decision making across the organization.<br>
Automated CI/CD pipelines with GitHub Actions, ensuring reliable and repeatable deployments across both layers.</p>
<p>Designed and deployed scalable ETL pipelines on AWS and Azure for enterprise clients, implementing star schema data models optimized for BI performance.<br>
Built Apache Spark and Databricks pipelines for a large-scale computer vision project, processing image datasets and implementing data augmentation to generate training data at scale.<br>
Created Tableau dashboards and optimized SQL query structures for performance and cost efficiency across cloud data warehouses.</p>
<p>Built full-stack web applications using Python/Django, ReactJS, AWS, and Docker.<br>
Delivered operational BI reports with Power BI and managed CI/CD pipelines with Jenkins and Git.</p>
<p>Built secure IT systems with Java and Oracle; automated enterprise workflows and implemented monitoring and reporting systems.</p>
<p>Architected and implemented a fully layered cloud data platform on Snowflake: raw ingestion → Data Vault 2.0 (Hubs, Links, Satellites) → denormalized publish layer optimized for BI consumption.</p><p> Built and maintained incremental ETL/ELT pipelines using Azure Data Factory with timestamp-based watermark loading of Parquet and CSV sources, ensuring efficient daily data loads at scale.</p><p> Implemented DataOps practices including automated data quality tests, duplicate detection, domain-specific validation logic, and real-time Slack alerting for pipeline failures.</p><p> Optimized Snowflake platform costs using transient tables, clustering keys, and query performance tuning, contributing to measurable infrastructure cost reduction.</p><p> Delivered Power BI dashboards on top of publish layer tables, translating complex data vault models into clean, stakeholder-ready analytics products.</p><p> Maintained full dev/prod environment parity with nightly automated promotion, reducing deployment errors and ensuring reliable business operations.</p><p> Coordinated cross-team data quality accountability with upstream API teams, establishing end-to-end pipeline ownership and incident resolution processes.</p><p> Managed SQL environments using SSMS for scheduling, stored procedures, and version-controlled pipeline logic.</p>
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