Data Engineer

• Lead end-to-end technical delivery of complex data and analytics solutions, ensuring scalability, reliability, and alignment to business outcomes.
• Design, build, and optimize data pipelines and analytical data models (batch and near real-time) to support enterprise reporting, BI, and advanced analytics use cases.
• Assess source systems and data quality, defining transformation logic, data standards, and controls to meet business definitions and governance requirements.
• Partner with business and technical stakeholders to translate requirements into technical designs, and contribute to estimation, planning, and delivery sequencing.
• Drive data architecture and engineering best practices, including modular design, reusability, performance optimization, and cost efficiency.
• Mentor and coach team members, elevating overall engineering capability through guidance, knowledge sharing, and hands-on support.
• Perform code and design reviews, ensuring adherence to standards, maintainability, and optimal performance of data solutions.
• Lead root cause analysis and issue resolution, implementing preventative measures and performance improvements across pipelines and platforms.
• Continuously improve delivery processes, identifying opportunities to increase speed, quality, automation, and reliability.
• Implement and enforce data governance practices, including lineage, documentation, data quality checks, and secure data handling.
• Collaborate with platform and infrastructure teams to ensure optimal use of cloud data platforms (e.g., Snowflake, Azure), including performance tuning and cost management.
• Support DevOps and CI/CD practices for data engineering, including automated testing, deployment pipelines, and environment management.
• Ensure operational excellence, including monitoring, alerting, and SLAs for critical data pipelines and data products.
• Contribute to platform strategy and tool selection, evaluating new technologies to enhance data engineering capabilities and self-service enablement.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• Deep experience with data transformation, ETL/ELT, and data management.
• Deep experience in writing and optimizing complex SQL.
• Experience with data modeling using dimensional modeling techniques, including star schemas.
• Experience with cloud infrastructure, cloud computing, and big data platforms.
• Understanding of structured, semi-structured, and unstructured data.
• Experience with data streaming, messaging, and event-driven data architecture.
• Knowledge of data governance and data quality frameworks.
• Experience working in an Agile environment.
• Experience working with NoSQL databases, such as graph, document, and column databases.
• Knowledge of machine learning and artificial intelligence concepts.
• Ability to articulate ideas with strong communication and presentation skills.
• Ability to work as part of a small team, share ideas, and contribute to strategic goals.