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Niche MDM/Databricks skillset reduces applicants despite metro location, hybrid work, and general Data Engineer title.
Specialized MDM tooling and master-data experience limit cross-industry transferability.
Multiple explicit years requirements and mandatory MDM, Databricks, and data modeling skills increase filtering strictness.
Own and develop the enterprise Master Data Management (MDM) data model and architecture, including design, deployment, and lifecycle management of MDM and Databricks platforms.
Lead and govern MDM best practices, standards, data quality, and compliance with security and privacy mandates across the enterprise.
Design and implement scalable, reliable data pipelines for machine learning using Apache Spark, Delta Lake, MLflow, and Databricks services; mentor team members and conduct technical design reviews.
6-10 years of hands-on experience in Data Management/Development with at least 5 years in data modeling using tools like CA Erwin or Embarcadero ER Studio.
5 years of experience in data warehousing and 2 years as a technical lead; 2 years experience with an MDM solution is mandatory.
Proficiency in Python, Scala, SQL; experience with Databricks, cloud platforms (AWS, Azure, or GCP); and mandatory experience with Informatica MDM and Oracle.
Bachelor's degree in Engineering, Computer/Data Science or equivalent experience; experience with Agile development methodology; mandatory technical skills include Data Warehousing, EAI, Metadata Management, MDM, Data Quality Management, SQL, PL/SQL, UML, XML.
Experienced in leading enterprise-level MDM projects with strong command over data modeling and quality governance in complex data environments.
Familiarity with implementing data pipeline solutions integrating machine learning platforms and modern data architecture (Databricks, Apache Spark).
Ability to evaluate and adopt emerging MDM and data quality technologies while ensuring compliance with security and privacy standards in a hybrid work environment.