Project Manager (Data & Analytics)

Responsibilities

• Project Planning & Execution: Define project scope, create project plans, manage milestones, and ensure delivery within budget and timelines
• Data Ingestion & Pipelines: Oversee the construction and maintenance of robust ingestion pipelines to move data from databases (RDBMS/Cloud Data Warehouses) and flat files (CSV, JSON, XML) into data lakes/warehouses.
• Data Quality & Validation: Establish data profiling and verification procedures to catch schema drift, data errors, and duplicates during ingestion, ensuring data integrity
• ETL/ELT Processes: Collaborate with data engineers to design and optimize data extraction, transformation, and loading routines using tools like Informatica IDMC, IBM Data Stage, Talend, or Apache Airflow.
• Stakeholder Management: Liaise with business users and technical teams to gather requirements, report status, and address risks.
• Vendor/Resource Management: Manage third-party vendors and internal teams, including staff allocation and work direction.
• Required Skills & Qualifications
• Experience : 5-8+ years of experience in data warehousing or data engineering project management.
• Data Technologies : Proficiency in cloud data warehouses (Databricks, Snowflake, Azure Synapse) and ETL/ingestion tools (INFORMATC/ DataStage).
• Technical Skills: Strong understanding of SQL, data modeling (Kimball/Inmon), data profiling, and orchestration platforms.
• Project Methodologies: Experience with Agile/Scrum or Waterfall project management methodologies.
• Education : Bachelor's degree in Computer Science, Information Systems, or a related field

Additional Information

Job Description Data Warehouse Project Manager is responsible for leading the end-to-end planning, implementation, and maintenance of data pipelines, ensuring that data is accurately extracted, transformed, and loaded (ETL) from diverse sources into a central data warehouse or data lake . This role bridging technical teams and business stakeholders focuses heavily on data quality, validation, and automation, specifically in the ingestion of flat files, APIs, and databases

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Company Intel

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