Product Manager – Enterprise Data Warehouse

1 week ago

Ras alKhaimah, Ra’s al Khaymah, United Arab Emirates BettingJobs Full time

BettingJobs is excited to present an exceptional opportunity in Ras Al Khaimah, UAE, for ambitious individuals looking to take on a career defining role.

Set to open in early 2027, our client is developing a landmark 72-floor resort in Ras Al Khaimah featuring a casino, hotel, multiple restaurants, and a marina.

In preparation for this, they are looking to add a Product Manager – Enterprise Data Warehouse to their corporate team.

Responsibilities:

  • Define and drive the vision, strategy, and roadmap for the enterprise data platform.
  • Champion modern data architectures (lakehouse, streaming pipelines, real-time analytics).
  • Develop long-term strategies for data storage, processing, governance, and activation.
  • Oversee end-to-end data pipelines (batch ingestion, real-time streaming, storage, transformation, activation).
  • Ensure best practices in ETL/ELT, schema design, and data quality.
  • Lead integration of real-time streaming technologies (Kafka, Flink, Confluent).
  • Implement data governance (metadata management, lineage tracking, data cataloging).
  • Ensure security and compliance (GDPR, CCPA, HIPAA).
  • Standardize data quality and master data management (MDM).
  • Collaborate with engineering, AI/ML, BI, marketing, and CRM teams for seamless data access.
  • Support MLOps (model training, tracking, deployment) with platforms like Databricks and MLflow.
  • Enable data activation via CRM integrations and omnichannel platforms.
  • Optimize cloud compute resources (Azure) for cost and performance.
  • Drive innovation in data storage, query optimization, and real-time analytics.
  • Evaluate and implement new data technologies for scalability and efficiency.

Requirements:

  • 7+ years of experience in product management, data engineering, or data platform architecture.
  • Deep knowledge of data warehousing (Snowflake) and data lake solutions (Azure Data Lake, Delta Lake, Iceberg).
  • Strong understanding of data engineering pipelines, ETL/ELT, and real-time streaming (Kafka, Flink, Spark, Databricks).
  • Hands-on experience with data governance tools, data catalogs, and lineage tracking (Coalesce, Atlan, Alation).
  • Familiarity with AI/ML platforms (Databricks, MLflow, Feature Stores, AutoML) and their data dependencies.
  • Strong experience in SQL, Python, and cloud-native data processing.
  • Ability to bridge technical and business requirements, working cross-functionally across multiple teams.
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