Job Summary
The Data Engineer is responsible for designing, building, and maintaining scalable, reliable, and secure data pipelines and platforms that support analytics, reporting, and operational use cases. The role focuses on data ingestion, transformation, storage, and quality assurance, enabling business teams, analysts, and data scientists to access high-quality, trusted data. Roles and Responsibilities
Data Pipeline & Infrastructure Development
- Design, develop, and maintain scalable data pipelines for data ingestion, transformation, and storage.
- Build and manage ETL/ELT workflows to integrate data from multiple internal and external systems.
- Develop and maintain data warehouses, data lakes, and hybrid storage solutions.
- Monitor and optimize pipeline performance, reliability, and cost efficiency.
Data Quality & Reliability
- Implement data quality checks, validations, and monitoring mechanisms.
- Identify and resolve data issues, anomalies, and pipeline failures.
- Ensure data accuracy, completeness, and consistency across platforms.
- Support continuous improvement of data reliability and availability.
Collaboration & Analytics Enablement
- Collaborate with data scientists, analysts, and business users to understand data requirements.
- Deliver curated, analytics-ready datasets to support reporting, dashboards, and advanced analytics.
- Partner with Data Architects and Enterprise Architecture teams to ensure alignment with architectural standards.
Data Security & Governance Support
- Implement data engineering solutions that comply with data governance, security, and privacy policies.
- Apply access controls, data masking, and other protective measures where required.
- Support metadata management, documentation, and lineage tracking.
Documentation & Automation
- Create and maintain documentation such as data flow diagrams, pipeline designs, and data dictionaries.
- Automate data processing and operational tasks to reduce manual intervention and errors.
- operational procedures and troubleshooting guidelines.
Technology Evaluation & Continuous Improvement
- Evaluate and adopt new data tools, platforms, and engineering practices.
- Stay current with trends in big data, cloud platforms, and data engineering frameworks.
- Recommend improvements to data infrastructure and processing efficiency
Work Location: In person
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