Data Catalog Implementation Specialist (m/w/d)

Michael PageWorkFromHome

Stellenbeschreibung:

Herausforderung

zeitnaher Start

About Our Client

Start: asap

Project Duration: 03 Months +

Workload: 5 days / per week

Location: Remote (95%) Düsseldorf

Industry: Sales

Project language: English/German (nice to have)

Job Description

We are seeking a hands-on Data Catalog Expert to lead a critical pilot project focused on democratizing data access for our Data Science team. The engagement involves setting up an initial data catalog instance and integrating it with our Databricks environment to register key source datasets. The objective is to demonstrate the value of a centralized metadata repository by transforming raw table lists into a searchable, context-rich asset library that accelerates model development and analytics.

Key Responsibilities

Pilot Configuration: Deploy and configure the initial instance of the data catalog (e.g., Atlan or DataHub) to support a Proof of Concept (PoC) scope. Work with the DataHub open-source framework within our ecosystem.
Needs Assessment: Consult on how effectively the data catalog meets organizational requirements and identify gaps.
Databricks Integration: Establish secure connectivity between the data catalog and Databricks Unity Catalog/Delta Lake to automate ingestion of schemas, tables, and views.
Metadata Enrichment: Execute automated metadata extraction and implement tagging strategies to classify sensitive data (PII) and add business context (descriptions, owners).
User Onboarding: Design a streamlined workflow enabling Data Scientists to search, query, and request access to datasets directly through the catalog interface.

Required Technical Skills

Data Cataloging: Proven experience configuring modern data governance platforms, specifically Atlan and/or DataHub.
DataHub Open Source Framework: Hands‑on knowledge of the DataHub open‑source catalog framework.
Data Platforms: Strong proficiency with Databricks (Lakehouse architecture, Delta Lake, Unity Catalog) and Spark SQL.
Metadata Management: Expertise in metadata ingestion frameworks, API‑based integration (REST/GraphQL), and automated classification policies.
Scripting: Proficiency in Python or SQL for custom connector configuration and metadata manipulation.
Governance: Familiarity with data stewardship principles, including ownership assignments, glossary creation, and certification workflows.

Pilot Deliverables

Functional connection between Databricks and the data catalog (Atlan/DataHub).
Registration of high‑priority source datasets with complete metadata (descriptions/tags).
Demonstrable "Search‑to‑Query" workflow for the Data Science team.
Final recommendation report on catalog scalability and long‑term architecture.