We are seeking an experienced Senior Data Engineer to join our growing data and analytics team. The ideal candidate will be responsible for designing, developing, and maintaining scalable data solutions that support business intelligence, analytics, reporting, and data-driven decision-making.
This role requires a strong technical professional with expertise in data architecture, ETL/ELT processes, data integration, and large-scale data management. You will work closely with business stakeholders, analysts, data scientists, and engineering teams to build reliable and efficient data pipelines that transform raw data into actionable insights.
The successful candidate should have a passion for solving complex data challenges, improving data quality, and implementing scalable solutions that support organizational growth and innovation.
Key Responsibilities
Data Engineering & Pipeline Development
- Design, build, and maintain scalable data pipelines for data ingestion, transformation, and processing.
- Develop efficient ETL/ELT workflows to support reporting, analytics, and operational systems.
- Integrate data from multiple internal and external sources while ensuring consistency and reliability.
- Optimize data processing workflows to improve performance, scalability, and efficiency.
Data Management & Quality Assurance
- Implement data quality frameworks, validation processes, and monitoring mechanisms.
- Ensure the accuracy, completeness, and integrity of enterprise data assets.
- Identify and resolve data inconsistencies, performance bottlenecks, and processing issues.
- Maintain data governance standards and support compliance requirements.
Collaboration & Stakeholder Support
- Partner with business teams, analysts, product managers, and technical stakeholders to understand data requirements.
- Translate business needs into scalable and effective data solutions.
- Support data-driven initiatives by providing reliable datasets and technical expertise.
- Collaborate with cross-functional teams to improve data accessibility and usability.
Documentation & Best Practices
- Create and maintain technical documentation, data flow diagrams, and system specifications.
- Establish and promote best practices for data engineering, data modeling, and data management.
- Participate in architecture discussions and contribute to data platform improvements.
- Support continuous improvement initiatives focused on automation, efficiency, and scalability.
Monitoring & Troubleshooting
- Monitor data pipelines and system performance to ensure uninterrupted operations.
- Troubleshoot and resolve data-related incidents in a timely manner.
- Perform root-cause analysis and implement long-term solutions for recurring issues.
- Continuously improve platform reliability and operational efficiency.
Required Skills
Technical Skills
- Strong understanding of data engineering principles, data architecture, and database concepts.
- Hands-on experience designing and maintaining large-scale data pipelines.
- Experience with ETL/ELT tools, data integration frameworks, and workflow orchestration platforms.
- Proficiency in SQL and database management systems.
- Experience working with structured, semi-structured, and unstructured datasets.
- Knowledge of data warehousing concepts and modern data platform architectures.
- Familiarity with cloud-based data services and distributed data processing environments.
- Experience with data quality management, validation frameworks, and monitoring solutions.
Professional Skills
- Excellent analytical and problem-solving abilities.
- Strong attention to detail and commitment to data accuracy.
- Effective communication and stakeholder management skills.
- Ability to work independently while collaborating within cross-functional teams.
- Strong organizational and documentation skills.
- Ability to manage multiple priorities in a dynamic environment.
Preferred Qualifications
- Experience working with Big Data technologies and cloud data platforms.
- Familiarity with data modeling, data governance, and master data management concepts.
- Knowledge of Python, Spark, or other data processing technologies.
- Exposure to business intelligence and analytics ecosystems.
- Experience supporting enterprise-scale data transformation initiatives.
Education
- Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Software Engineering, or a related technical discipline.
- Relevant certifications in Data Engineering, Cloud Technologies, Big Data, or Analytics are an added advantage.
