Data Architect - Berlin, Deutschland - Top Remote Talent

Top Remote Talent

Geprüftes Unternehmen

Berlin, Deutschland

vor 3 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

The team of experts providing analytical services to healthcare clients is looking for a great, long-term Data Architect.

You will join an international team of first-class professionals who are passionate about creating products that improve the quality of medical services.

The company offers exposure to a variety of industries and technologies, room to grow as a professional, time in projects to learn new skills and an opportunity to work with phenomenal coworkers, some of the best people on the planet.

Fundamentals:

Extensive OLTP and OLAP experience in terms of both SQL and Data Architecture.
Handson experience with Data Modeling and Dataintensive Architecture.
Ability to help engineers with 3NF normalization.
Ability to help engineers with Query Optimization.
Ability to provide guidelines on DWH Data Modeling both for Batching and Streaming architectures. DWH Modeling with a robust, fast, distributed, and modern architecture.
Understanding of EAV Modeling.
Understanding of Dimensional Modeling and SCD.
Understanding of Bi-Temporal History, Time-Variant Analysis.
Understanding of pros and cons of different Datarelated concepts and methodologies: DWH vs Data Lake vs LakeHouse vs Data Mesh, Lambda vs Kappa, Inmon vs Kimball vs Data Vault vs Anchor Modeling. Handon experience with at least one of the following (or similar) instruments: Spark / Flink / Beam.

-
Pros:

Experience with GCP or Cloud-Native Engineering.
Handson experience with Streaming.
Handson experience with Kafka.
Experience with Data Gov / Lineage and Data Quality.
Experience with Python.
Hopefully, "a Unix guy" and not a Windows Vista user.

Limitations:

Ability to work up to 1314 EST. Probably, any Asian Time Zone is not acceptable at all.
We'll never use Oracle.
We'll never use Java. Probably, even for custom DataFlow templates.
We may never use Scala.
We're mostly using GCPnative stack. "Framework engineering" isn't a top priority.

Our stack: