Senior Data Engineer pro Data CoE

Hybrid

Lokace: Praha, Stodůlky

Jazyk: ČJ, AJ technická na velmi dobré úrovni

Level: senior

Forma spolupráce: IČO

Nástup: Září 2026

Alokace: full-time, HO 50:50 (2+3 a 3+2)

Délka spolupráce: alespoň jeden rok

• In Data CoE, we are seeking a senior consultant to support our migration to Databricks as a hands-on expert advisor. The consultant will support internal teams by helping to shape and define architecture-related decisions, DevOps and data management cycle standards and best practices, and clarify options with their consequences with emphasis on efficiency and migration time constraints

What will you do?

• • Databricks architecture and engineering patterns,
    • practical guidance on how to structure workloads, delivery patterns, standards and reusable technical approaches in Databricks.
    • Governance/data management lifecycle
    • Metadata management, tagging, dictionaries, catalogs, Unity Catalog setup, native options
    • Data quality / DQMS approach help evaluate options for data quality management, including native Databricks monitoring/profiling capabilities, process implications, and possible hybrid approaches.
    • Airflow and orchestration
    • advice on orchestration options and trade-offs, including how Airflow can be used with Databricks jobs + best practices/standards
    • DevOps setup
    • support in defining practical approaches in terms of CICD, metadata/template driven approach, monitoring and ops, connection to dictionaries and catalogs (incl. tools)
    • standards and conventions — support in defining basic standards such as naming conventions, repository structure, deployment principles, and selected governance guardrails.

The proposed consultant should have:

• • Strong hands-on Databricks experience in real delivery environments
    • Practical experience with Airflow orchestration in combination with Databricks
    • Strong CI/CD experience for Databricks, including Git-based development, testing, deployment automation, operations
    • Experience with data quality / monitoring / DQ frameworks, ideally in Databricks or similar modern data platforms.
    • Ability to provide practical recommendations, alternatives, and consequences of choices
    • Ability to work in a supporting, collaborative mode