Data Engineering Senior Manager

Responsible for leading and managing the organization’s data engineering function, ensuring the development and delivery of scalable, reliable, and high-performing data platforms and pipelines aligned with business objectives. This role focuses on defining data engineering standards, practices, and frameworks to enable efficient data ingestion, transformation, integration, and processing across cloud and on-premises environments. The Data Engineering Manager leads and mentors data engineers, oversees engineering projects and delivery, and collaborates closely with data architecture, governance, analytics, and business teams to ensure data solutions are secure, governed, high-quality, and optimized for performance and scalability.

Data Architecture & Design:

· Develop and maintain a scalable, secure, and high-performance data architecture aligned with business needs.

· Ensure seamless data flow between systems, integrating structured and unstructured data sources.

· Define and implement best practices for data storage, processing, and security.

Select and optimize data platforms, tools, and technologies for efficiency and cost-effectiveness.

Data Modeling:

· Design and implement data models (dimensional, relational, Data Vault, NoSQL) to support operational and analytical use cases.

· Ensure consistency, accuracy, and integrity of data models across the organization.

· Continuously refine models to improve performance and adaptability to evolving business requirements.

Establish and enforce data modeling standards and governance.

Data Engineering and Pipeline Development:

· Build, optimize, and maintain automated ETL/ELT pipelines to process large-scale data.

· Troubleshoot, debug and upgrade existing ETL solutions.

· Enhance data infrastructure to support efficient storage, retrieval, and transformation.

· Implement automation to reduce manual processes and increase workflow efficiency.

· Ensure data pipelines are robust, scalable, and support real-time and batch processing.

Identify data discrepancies and develop data metrics.

Innovation & Technology Adoption:

· Stay up to date with the latest trends in data architecture, engineering, and analytics to identify and implement innovative solutions.

· Research and integrate cutting-edge tools and platforms to enhance data management capabilities and streamline processes.

· Encourage a team culture focused on continuous improvement, supporting experimentation with new technologies and methodologies.

Develop and execute a technology roadmap to ensure the team is using the best tools available to meet both current and future data needs.

Optimization & Performance Management: