Responsibilities !

• Data Warehouse Strategy: Develop & implement a comprehensive data warehousing strategy, in conjunction with the Enterprise IT function, aligned with the organization's strategic objectives.
• Team Leadership: Lead, mentor, and develop a team of data engineers (direct and indirect reports), setting clear goals and providing mentorship.
• Architectural Design: Input into the Design and maintenance of an efficient and scalable data warehouse architecture, considering data modelling, storage, and retrieval performance.
• ETL Processes: Oversee the development of Extract, Transform, Load (ETL) processes to ensure data is collected, transformed, and loaded accurately into the data warehouse from golden source systems.
• Data Governance: Implement and enforce data governance policies, including data quality standards, data security, and compliance with relevant legislation (e.g., GDPR).
• Data Integration/Ingestion: Collaborate with other teams to ingest various data sources (internal and external) into the data warehouse, ensuring data consistency and accuracy.
• Data Modelling: Work on data modelling to build a logical representation of data that meets the needs of data analysts and business users.
• Data Documentation: Ensure comprehensive documentation of data warehouse processes, schemas, and ETL pipelines for knowledge sharing and future reference.
• Project Management: Plan, implement, and supervise data warehouse projects, including resource allocation, timelines, and budget management.
• Cross-functional Collaboration: Collaborate with business leaders, data scientists, and other stakeholders to understand their data requirements and provide solutions. Collaborate with IT for development of data-models.
• Reporting and Analytics: Support the development of reporting & analytics tools for end-users, ensuring they have access to accurate and timely data.
• Data products: Orchestrate development of standard data products (views) that can be used by different stakeholders such as business end users (self-service), data scientists, and others for different purposes.

Qualifications:

• Demonstrable experience (typically 10+ years) in data engineering, with a focus on data warehousing.
• Proficiency in data warehousing methodologies and tools (e.g., SQL, ETL tools, data modelling).
• Proficient Knowledge of cloud-based data warehousing platforms (e.g., AWS, Azure Data Warehouse)
• Familiarity with data visualization and reporting tools (e.g., Power BI, SAP Analytics cloud)
• Familiarity of AWS Snowflake database and SAP HANA is preferable.
• Familiarity with People (HR) Data is preferable.
• Strong leadership and team management skills.
• Deep knowledge of data governance principles and data security.
• Excellent problem-solving and analytical abilities.
• Strong project management skills with the ability to manage multiple projects concurrently.
• Excellent communication and interpersonal skills for collaborating with cross-functional teams.
• Dedication to staying current with industry trends and standard processes.

To be successful in the role:

Project Delivery:

• Project Timelines: Meeting or exceeding project timelines for data warehousing initiatives, including Snowflake-related projects.
• Project Budget: Staying within budget.

Data Integration and Accessibility:

• Data Integration: Successful integration of diverse data sources into the data warehouse, ensuring data consistency and availability.
• Accessibility: Ensuring that end-users have easy access to relevant data and analytics tools, promoting self-service analytics.

Data Security and Compliance:

• Data Security: Ensuring data security measures are effective and that there are no data breaches or vulnerabilities.
• Regulatory Compliance: Demonstrating compliance with relevant data privacy regulations (e.g., GDPR, CCPA).

Cross-functional Collaboration:

• Stakeholder Satisfaction: High satisfaction levels among business analysts, data scientists, and other stakeholders with the data and insights provided by the data warehouse.
• Alignment with Business Goals: Demonstrating that data warehouse initiatives align with and contribute to the achievement of the organization's strategic goals.

Documentation and Knowledge Sharing:

• Comprehensive Documentation: Maintaining up-to-date documentation of data warehouse processes, schemas, and ETL pipelines.
• Knowledge Sharing: Promoting knowledge sharing and cross-training within the team to ensure continuity and skill development.