POSITION SUMMARY
Design, build, and maintain scalable data transformation pipelines that support the enterprise data platform.
This role is responsible for transforming raw operational data into structured and reliable datasets aligned with enterprise architecture standards. The position ensures that data pipelines are reusable, performant, and consistent, supporting analytics, machine learning workflows, and digital platform capabilities.
Operating within a structured data architecture, this role collaborates closely with IT teams responsible for data ingestion, Senior Analytics Engineers responsible for curated datasets, and Data & Analytics teams responsible for analytics and reporting. The role requires strong hands-on technical capabilities in data transformation, performance optimization, and pipeline design.
AREAS OF RESPONSIBILITY
Data Pipeline Development and Transformation
- Develop and maintain structured data transformation pipelines within the enterprise data platform.
- Transform raw operational data into structured datasets aligned with defined architecture standards.
- Implement reusable transformation logic to support consistent data processing.
- Optimize transformation processes to improve performance and scalability.
- Maintain structured workflows that support continuous data processing.
Data Integration Alignment and Data Quality Support
- Collaborate with IT teams responsible for data ingestion to validate data availability and readiness.
- Perform validation checks to ensure accuracy and completeness of transformed datasets.
- Implement structured validation and reconciliation processes.
- Identify and resolve data inconsistencies across pipeline stages.
- Support continuous monitoring of data pipeline health.
Data Transformation Optimization and Performance Tuning
- Analyze transformation performance and identify opportunities for improvement.
- Optimize query logic, transformation workflows, and pipeline structures.
- Ensure transformation processes are scalable and efficient under increasing data volumes.
- Maintain structured code practices that support maintainability and reuse.
- Support performance tuning initiatives across transformation pipelines.
Collaboration with Modeling and Analytics Teams
- Work closely with Senior Analytics Engineers to align transformation outputs with curated dataset requirements.
- Support Data & Analytics teams by ensuring availability of structured datasets for analysis.
- Participate in cross-functional delivery initiatives involving data platform enhancements.
- Ensure transformation outputs are aligned with defined data structures and business needs.
Engineering Discipline and Continuous Improvement
- Maintain structured documentation for transformation workflows and data logic.
- Follow established engineering standards and governance practices.
- Participate in knowledge-sharing initiatives within the team.
- Identify opportunities to improve pipeline design and operational efficiency.
- Support adoption of emerging tools and best practices.
Performance Management
- Support structured delivery of assigned pipeline responsibilities and maintain alignment with team objectives.
Coaching and Mentoring
- Share technical knowledge and support collaborative learning across team members.
Employee Engagement
- Contribute to team initiatives that promote collaboration, ownership, and continuous learning.
QUALIFICATIONS
Education
Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, or a related discipline.
Relevant Experience
- Minimum 3 years of experience in data engineering, data transformation, or pipeline development roles.
- Strong hands-on experience building structured data pipelines and transformation workflows.
- Experience working with modern data warehouse environments.
- Experience writing and optimizing SQL-based transformations.
- Familiarity with structured data architecture and pipeline development practices.
Licenses / Certifications
Relevant certifications in data engineering or cloud data platforms
platforms are an advantage.
Business Understanding
- Strong understanding of business reporting and analytics workflows.
- Familiarity with KPI development and business metric alignment.
- Ability to translate business requirements into technical data structures.