Senior Data Engineer
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- 01.01.2026
Kurzvorstellung
Qualifikationen
Projekt‐ & Berufserfahrung
1/2024 – offen
Tätigkeitsbeschreibung…
Eingesetzte QualifikationenBig Data, Data Warehousing, Databricks, Generative KI, MLOps
7/2019 – 12/2023
Tätigkeitsbeschreibung
- As the lead data engineer, successfully led the engineering of data processing pipelines in multiple high-profile projects, utilizing cutting-edge technologies like Apache Spark to efficiently process structured and semi-structured data. These pipelines improved processing times, scalability, and data quality for the organization.
- Collaborated with Data Scientists to develop data-driven solutions that achieved seven-digit cost savings and revenue increase for the organization. Through close collaboration and a deep understanding of the business requirements, we identified critical areas for improvement and designed innovative solutions that delivered substantial value. Additionally, established MLOps approach with AWS Sagemaker for streamlined development and deployment of machine learning models.
- Successfully led a project on implementing dynamic pricing for automotive insurance, which enabled the business to access near-real-time data for pricing optimizations. Managed the end-to-end project lifecycle, including requirements gathering, project planning, team coordination, and stakeholder management. Delivered the project within the allocated budget and timeline, resulting in increased revenue and improved customer satisfaction.
Amazon Web Services (AWS), Apache Spark, Datenmodelierung, ETL, Projektmanagement (IT), Python, Requirement Analyse
4/2019 – 6/2019
Tätigkeitsbeschreibung
• Successfully migrated complex vert.x based microservices to Self-Containing Services using Spring Boot, Spring Cloud, and
Apache Camel. Overcame numerous challenges during the migration process, resulting in a more maintainable system.
• Led the effort to perform comprehensive performance testing of existing infrastructure using jMeter and implemented
integration tests to ensure system reliability and scalability. As a result of these efforts, we were able to identify and resolve
critical performance bottlenecks, resulting in a more efficient and robust system.
Java (allg.), Spring Framework, Apache Camel
4/2018 – 4/2019
Tätigkeitsbeschreibung
Building a scalable big data pipeline moving streaming data and batched data to a data lake on AWS Cloud:
• Data integration from variety of data sources (e.g. relational data from IBM Mainframe, Oracle, MS SQL Server, SAP BW and web services data from check24, German weather portal) with Informatica Data Integration Tool and AWS Glue
• Writing python code for automatic generation of Informatica mappings, sessions and workflows
• Deployment of Streaming Platform Confluent Kafka, which is one of the main building blocks for data ingestion process
• Deployment of AWS S3 storage for persistent storages of unstructured and semi-structured data within the data lake
• Deployment of a cloud-based data warehouse AWS Snowflake for persistent storages of structured and semi-structured data within the data lake
ETL, Amazon Web Services (AWS), Apache Spark, Big Data, Cloud Computing, Data Warehousing, Python
Ausbildung
Technische Universtität Berlin
Weitere Kenntnisse
- Databricks
- AWS SageMaker/EMR/Athena
- Google BigQuery
- PostgreSQL
- DB2
- Oracle
ETL / ELT Tool:
- Python (Jupyter Notebook)
- AWS EMR, Glue
- DBT
- Apache Kafka
- Informatica Data Integration
Data Warehouse Modeling:
- Dimension Modeling
- Data Vault Modeling
Cloud technology:
- Amazon Web Services
- Google Cloud
Programming skills:
- Python
- SQL
- Scala
- Java
Advanced Analytic / Machine Learning:
- Spark MLlib
- Scikit-learn
- Keras
Persönliche Daten
- Deutsch (Muttersprache)
- Englisch (Fließend)
- Europäische Union
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