Senior Data Engineer

Responsible for designing, building, and maintaining robust data pipelines and platforms that enable reliable data flow across the organization. This role focuses on developing scalable and high-performance data solutions, optimizing data processing, and ensuring data availability for analytics and business applications. The Senior Data Engineer will leverage expertise in modern data engineering practices, cloud technologies, and big data platforms to deliver efficient, secure, and well-governed data ecosystems.

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:

• 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.

Educational Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.

Advanced degrees provide deeper technical and strategic insights, enabling the professional to drive complex data solutions and innovations at the organizational level.

Special Certification or Training Required:

• Certified Data Management Professional (CDMP) – Preferred for expertise in data architecture and governance is preferred.
• Microsoft Certified: Azure Data Engineer Associate or AWS Certified Data Engineer Associate is preferred.

Required Industry Experience:

• 3-6 years of experience in Data Engineering, and Data Modeling with a proven track record of leading large-scale enterprise data initiatives.
• Expertise in designing, implementing, and optimizing complex data ecosystems in hybrid environments.
• Strong background in data governance, security, compliance, and risk management across enterprise systems.
• Hands-on experience managing data integration, migration, and transformation projects in a large-scale business setting.

Technological Requirements:

• Deep expertise in architecting, optimizing, and managing both relational (e.g., Oracle, PostgreSQL) and NoSQL (e.g., MongoDB, Cassandra) databases.
• Proven track record of designing and implementing scalable, enterprise-level Data Warehouse (DWH) architectures.
• Expert-level proficiency in writing and tuning complex SQL queries, stored procedures, functions, and triggers for high-performance data processing.
• Strong analytical skills related to structured or unstructured data sets.
• Advanced expertise in data modeling techniques, including Data Vault, Dimensional Modeling, and Entity-Relationship Modeling.
• Extensive experience in building and maintaining robust, scalable data pipelines (ETL/ELT) using modern technologies
• Expert programming skills in Python and SQL for data engineering and automation tasks, along with a strong understanding of version control (Git)
• Proven expertise in architecting and managing data lakes and data warehouses for big data environments.

Language Requirements: Fluent in English.