Professional Summary Data, Platform & Analytics Engineer with several years of experience designing scalable data pipelines, cloud-native architecture
Aktualisiert am 26.05.2026
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 26.05.2026
Verfügbar zu: 100%
davon vor Ort: 100%
Data Platform Engineering
Cloud Architecture
Snowflake
English
Fluent
German
Intermediate (A2, working on integration and professional improvement)

Einsatzorte

Einsatzorte

Berlin (+50km)
Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

3 Jahre 10 Monate
2022-01 - 2025-10

Designed and optimized end-to-end data pipelines

Data Engineer
Data Engineer
  • Designed and optimized end-to-end data pipelines using Python, Azure, and Snowflake.
  • Developed data products with Power BI dashboards, improving reporting queries for speed and cost.
  • 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.
  • Architected and implemented a fully layered cloud data platform on Snowflake: raw ingestion ? Data Vault 2.0 (Hubs, Links, Satelites) ? denormalized publish layer optimized for BI consumption.
  • Implemented DataOps practices including automated data quality tests, duplicate detection on Hub business keys, domain-specific validation logic, and real-time Slack alerting for pipeline failures.
  • Maintained full dev/prod environment parity with nightly automated promotion, reducing deployment errors and ensuring reliable business operations.
  • Optimized Snowflake platform costs using transient tables for staging layers, clustering keys, and query performance tuning contributing to measurable infrastructure cost reduction.
  • Delivered Power BI dashboards on top of publish layer tables, translating complex data vault models into clean, stakeholder-ready analytics products.
  • Coordinated cross-team data quality accountability with upstream API teams, establishing end-to-end pipeline ownership and incident resolution processes.
  • Managed SQL environments using SSMS for scheduling, stored procedures, and version-controled pipeline logic.
Solita GmbH, Berlin, Germany
1 Jahr 1 Monat
2019-01 - 2020-01

Designed and deployed scalable ETL pipelines on AWS and Azure

Data Engineer
Data Engineer
  • Designed and deployed scalable ETL pipelines on AWS and Azure for enterprise clients, implementing star schema data models optimized for BI performance.
  • Built Apache Spark and Databricks pipelines for a large-scale computer vision project, processing poster image datasets and implementing data augmentation to generate training data at scale.
  • Created Tableau dashboards and optimized SQL query structures for performance and cost efficiency across cloud data warehouses.
Solita AB, Stockholm, Sweden

Aus- und Weiterbildung

Aus- und Weiterbildung

2012 ? 2015

M.Sc. Internet Technology and Architecture | TU - Berlin & KTH - Stockholm

Focus: Internet Security, Network Architecture, Middleware, Open Source


2006 ? 2010

B.Sc. Information Technology Engineering | Mekelle Institute of Technology, Ethiopia

Focus: OOP, Web Development, Cryptography, Databases


Certifications

  • Azure Data Engineer Associate
  • Databricks Certified Data Engineer Associate
  • Databricks Lakehouse Fundamentals

Kompetenzen

Kompetenzen

Top-Skills

Data Platform Engineering Cloud Architecture Snowflake

Produkte / Standards / Erfahrungen / Methoden

Professional Summary

Data, Platform & Analytics Engineer with several years of experience designing scalable data pipelines, cloud-native architectures, and business intelligence solutions on Azure, AWS, GCP, and Databricks. Proven track record of delivering end-to-end data products from ingestion and warehousing on Snowflake to stakeholder-ready dashboards in Power BI and Tableau. Skilled at bridging technical complexity and business needs, driving measurable impact through data-driven architecture and cross-functional collaboration.


CORE TECHNICAL SKILLS

  • Cloud Platforms: Azure (primary), AWS, GCP and Databricks pipeline design, storage, compute, cloud-native architecture
  • Data Warehousing: Snowflake (primary), BigQuery ? clustering keys, transient tables, query optimization, cost management, time travel
  • Data Modeling: Data Vault 2.0 (Hubs, Links, Satelites), Dimensional Modeling, Star Schema, Publish Layer Design
  • Pipeline & ETL/ELT: Azure Data Factory, Apache Airflow, dbt, Incremental Loading, Watermark/Timestamp strategies
  • Streaming & CDC: Apache Kafka, Debezium (CDC), real-time pipeline design, PostgreSQL (primary relational DB)
  • Big Data & Lakes: Apache Spark, Databricks, Delta Lake, Apache Iceberg, Apache Hudi
  • Infrastructure & DevOps: Docker, Kubernetes, Terraform (IaC), GitHub Actions, Jenkins, CI/CD pipelines
  • Data Quality: Custom validation logic, duplicate detection, Slack-integrated alerting, automated test frameworks
  • BI & Reporting: Power BI, Tableau, semantic layer design, stakeholder dashboards

Programmiersprachen

Python
SQL
Scala
Java
R
data processing, automation, scripting

Einsatzorte

Einsatzorte

Berlin (+50km)
Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

3 Jahre 10 Monate
2022-01 - 2025-10

Designed and optimized end-to-end data pipelines

Data Engineer
Data Engineer
  • Designed and optimized end-to-end data pipelines using Python, Azure, and Snowflake.
  • Developed data products with Power BI dashboards, improving reporting queries for speed and cost.
  • 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.
  • Architected and implemented a fully layered cloud data platform on Snowflake: raw ingestion ? Data Vault 2.0 (Hubs, Links, Satelites) ? denormalized publish layer optimized for BI consumption.
  • Implemented DataOps practices including automated data quality tests, duplicate detection on Hub business keys, domain-specific validation logic, and real-time Slack alerting for pipeline failures.
  • Maintained full dev/prod environment parity with nightly automated promotion, reducing deployment errors and ensuring reliable business operations.
  • Optimized Snowflake platform costs using transient tables for staging layers, clustering keys, and query performance tuning contributing to measurable infrastructure cost reduction.
  • Delivered Power BI dashboards on top of publish layer tables, translating complex data vault models into clean, stakeholder-ready analytics products.
  • Coordinated cross-team data quality accountability with upstream API teams, establishing end-to-end pipeline ownership and incident resolution processes.
  • Managed SQL environments using SSMS for scheduling, stored procedures, and version-controled pipeline logic.
Solita GmbH, Berlin, Germany
1 Jahr 1 Monat
2019-01 - 2020-01

Designed and deployed scalable ETL pipelines on AWS and Azure

Data Engineer
Data Engineer
  • Designed and deployed scalable ETL pipelines on AWS and Azure for enterprise clients, implementing star schema data models optimized for BI performance.
  • Built Apache Spark and Databricks pipelines for a large-scale computer vision project, processing poster image datasets and implementing data augmentation to generate training data at scale.
  • Created Tableau dashboards and optimized SQL query structures for performance and cost efficiency across cloud data warehouses.
Solita AB, Stockholm, Sweden

Aus- und Weiterbildung

Aus- und Weiterbildung

2012 ? 2015

M.Sc. Internet Technology and Architecture | TU - Berlin & KTH - Stockholm

Focus: Internet Security, Network Architecture, Middleware, Open Source


2006 ? 2010

B.Sc. Information Technology Engineering | Mekelle Institute of Technology, Ethiopia

Focus: OOP, Web Development, Cryptography, Databases


Certifications

  • Azure Data Engineer Associate
  • Databricks Certified Data Engineer Associate
  • Databricks Lakehouse Fundamentals

Kompetenzen

Kompetenzen

Top-Skills

Data Platform Engineering Cloud Architecture Snowflake

Produkte / Standards / Erfahrungen / Methoden

Professional Summary

Data, Platform & Analytics Engineer with several years of experience designing scalable data pipelines, cloud-native architectures, and business intelligence solutions on Azure, AWS, GCP, and Databricks. Proven track record of delivering end-to-end data products from ingestion and warehousing on Snowflake to stakeholder-ready dashboards in Power BI and Tableau. Skilled at bridging technical complexity and business needs, driving measurable impact through data-driven architecture and cross-functional collaboration.


CORE TECHNICAL SKILLS

  • Cloud Platforms: Azure (primary), AWS, GCP and Databricks pipeline design, storage, compute, cloud-native architecture
  • Data Warehousing: Snowflake (primary), BigQuery ? clustering keys, transient tables, query optimization, cost management, time travel
  • Data Modeling: Data Vault 2.0 (Hubs, Links, Satelites), Dimensional Modeling, Star Schema, Publish Layer Design
  • Pipeline & ETL/ELT: Azure Data Factory, Apache Airflow, dbt, Incremental Loading, Watermark/Timestamp strategies
  • Streaming & CDC: Apache Kafka, Debezium (CDC), real-time pipeline design, PostgreSQL (primary relational DB)
  • Big Data & Lakes: Apache Spark, Databricks, Delta Lake, Apache Iceberg, Apache Hudi
  • Infrastructure & DevOps: Docker, Kubernetes, Terraform (IaC), GitHub Actions, Jenkins, CI/CD pipelines
  • Data Quality: Custom validation logic, duplicate detection, Slack-integrated alerting, automated test frameworks
  • BI & Reporting: Power BI, Tableau, semantic layer design, stakeholder dashboards

Programmiersprachen

Python
SQL
Scala
Java
R
data processing, automation, scripting

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