Data Engineer

Responsible for designing, building, and maintaining data pipelines that ensure reliable and efficient data flow across the organization. This role involves developing scalable data solutions, integrating data from multiple sources, and supporting analytics and business applications. The Data Engineer applies expertise in database management, cloud technologies, and modern data engineering practices to deliver secure, accurate, and well-structured data.

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\:

· Define and enforce data governance policies, ensuring compliance with GDPR, and other regulations.

· Monitor and optimize database performance, query execution, and storage efficiency.

· Manage data quality, metadata, and lineage to ensure reliability and transparency.

· Implement proactive monitoring and alerting for data infrastructure health.

Monitor performance and quality control plans to identify improvements.

Collaboration with Cross-Functional Teams\:

· Work closely with data scientists, business analysts, and BI teams to understand data requirements and design solutions that enable advanced analytics, reporting, and decision-making.

· Act as a bridge between business units and IT, ensuring that data architecture and engineering solutions meet both technical and business needs.

Lead efforts to optimize data workflows and processes across departments, driving efficiency and alignment in data operations