Data Engineer

Title: Data Engineer
Duration: 6 months
Location: Remote
Profile Summary:

The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and systems that deliver trusted data for analysis and product use cases. This role partners with cross-functional teams to understand data needs and implement solutions that support both near-term and long-term objectives. This role requires the ability to contribute to technical design, ensure data quality, and operate with increasing independence and accountability.

Profile Description:

• Develop and maintain batch and streaming data pipelines using modern tools and frameworks. Design transformations, optimize performance, and ensure reliable data delivery.
• Design and implement scalable and maintainable data models and storage solutions that align with business needs and support efficient querying, analysis, and data integration efforts.
• Engage in agile best practices, help refine stories, identify dependencies, and proactively raise risks or concerns to ensure work is completed on time or escalated when needed.
• Implement and enforce data quality controls, validation, and compliance standards across pipelines.
• Support the deployment, scheduling, and monitoring of data pipelines and workflows to ensure consistent, reliable execution.
• Maintain comprehensive documentation and advocate for coding standards, best practices, and reusable components.
• Collaborate regularly with cross-functional teams to clarify data requirements, document assumptions, and deliver high-quality solutions. Communicate clearly during stand-ups, design discussions, and retrospectives. Actively contribute to team code reviews and share learnings with peers.

Knowledge & Experience:

• 2-5 years of experience in data engineering, data modeling, and ETL pipelines.
• Proficient in SQL and Python for creating, improving, and fixing data pipelines.
• Experience with cloud and data platforms, especially Azure and Databricks (Delta Live Tables and Unity Catalog).
• Strong understanding of tools like SnapLogic, Azure Data Factory, and Jenkins for data integration and orchestration.
• Practical experience with Terraform for infrastructure as code and managing deployment pipelines.
• Experience integrating with APIs.
• Knowledge of data quality and monitoring tools, particularly Soda or similar.
• Proficient in version control and CI/CD workflows, using tools like GitHub.
• Solid understanding of data modeling principles (e.g., dimensional modeling, normalization).
• Comfortable working in agile teams, with a proactive approach to planning, organizing tasks, and collaborating.