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Jannis Koch

Data Engineer
  • Unverbindlicher Tarif
    840 € /Tag
  • Berufserfahrung3-7 Jahre
  • Antwortrate100 %
  • Antwortzeit4h
Das Projekt startet erst, wenn Sie das Angebot von Jannis annehmen
Standort und Mobilität
Standort
Stuttgart, BW, Deutschland
Ist bereit bei Ihnen im Büro zu arbeiten, in
  • Stuttgart und Umkreis (bis zu 50 km)
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Fähigkeiten (20)
Branchenkenntnisse
Jannis in wenigen Worten
I am a Data Engineer with 5 years of practical experience developing scalable ETL pipelines and data processing applications, both batch and real-time streaming.

I enjoy developing software and architectures with best practices in mind, striving for reliable and maintainable solutions.

I love to work in agile environments, solving technological challenges and delivering business value as early as possible at the same time.

My focus:
- Python, Java, Scala, Kotlin
- Spark (Batch / DStreams / Structured Streaming)
- Kafka (Producer-Consumer API / Kafka Streams)
- Cloud technologies (terraform, AWS)
- Container technologies (docker / kubernetes)
- Tooling for every part of the software lifecycle (git, CI/CD, extensive integration testing, test automation, logging, monitoring)
Projekt- und Berufserfahrung
  • Insurance
    Real-Time Data Integration
    BANKEN & VERSICHERUNGEN
    Dezember 2022 - Heute (2 Jahre und 1 Monat)
    Data from various source systems is ingested from Kafka, then enriched and transformed for the requirements of target systems in Kafka Streams Java microservices.  Kafka Connect is used to transfer the data to the target systems, e.g. via JDBC.
    - Apache Kafka (Kafka Streams, Kafka Connect)
    - Java, Quarkus
    - Red Hat OpenShift
    - Splunk, SignalFX
  • Media Industry
    Data Engineer (Kafka / real-time processing and integration)
    DIGITALAGENTUREN & IT-CONSULTING
    Februar 2021 - November 2022 (1 Jahr und 10 Monate)
    Customer data (e.g. sales data, web site click stream) was ingested as a data stream from various source systems and integrated in real-time into a marketing automation platform. User characteristics such as customers content preferences were extracted in real-time, enabling the customer relations team to achieve precise customer segmentation and provide customers with highly personalized communication.

    - Apache Kafka (Kafka Streams)
    - Migration of legacy AWS Kinesis data
    - Apache Spark (DStreams API, Structured Streaming)
    - Kubernetes, kustomize
    - CI/CD (Gitlab CI)
    Apache Kafka Kafka Spark Kubernetes AWS CI/CD Python Kotlin
  • Media Industry
    Data Engineer (Spark / ETL)
    DIGITALAGENTUREN & IT-CONSULTING
    Mai 2020 - Februar 2021 (9 Monate)
    Customer data from various source systems was integrated with target systems of company-internal stakeholders in the commercial department to enable reporting and analysis.
    - Created reliable data pipeline applications to ingest the data in a data lake, process and combine the data and load it into database systems
    - Comprehensive concept for complying with GDPR and company privacy policies for public clouds using encryption
    - Kubernetes / spark-on-k8s, kustomize
    - Airflow for workload scheduling
    - AWS cloud technologies (S3, RDS), terraform
    - Pyspark
    - CI/CD (Gitlab CI)
    Airflow Spark AWS Kubernetes CI/CD
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