Data Engineer, Behaviour Lab

• A bachelor's, master's degree or PhD in Computer Science, Electrical Engineering, Data Science, or a related field
• 3+ years of hands-on experience as a data engineer, building ETL pipelines, financial data processing solutions, and owning the data lifecycle management (from data quality monitoring to storage, etc.)
• Strong working knowledge of Python and related infrastructure, including AWS Glue and AWS Lambda, working with analytics services, APIs, optimizing performance, and working with cloud environments such as AWS
• Deep understanding of data science toolkits such as Jupyter Notebook, NumPy, Pandas, scikit-learn, etc
• Experience working with relational databases, with a strong understanding of data modelling and schema design, specifically with Postgres
• Experience building solutions on AWS and leveraging cloud service provider infrastructure to handle high amounts of data processing at scale
• Knowledge of engineering best practices and processes such as TDD, code reviews, code refactoring, branch management, and CI/CD practices is essential
• Excellent troubleshooting and problem-solving skills, with the ability to debug complex systems and data issues
• Strong communication skills both verbally and in writing, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders
• Experience mentoring junior data engineers and providing guidance on best practices and processes
• A strong commitment to continuous learning and self-improvement, with the ability to keep up to date with the latest technologies and tools in the data engineering field

Desirable

• Designing, implementing, and maintaining large-scale data processing systems on AWS, ensuring that they are scalable, efficient, and reliable
• Leading the design and development of ETL pipelines that transform raw data into usable data sets for analysis
• Owning the data lifecycle management, from data quality monitoring to storage, ensuring that data is properly classified, stored, and secured
• Collaborating with data scientists and analysts to understand their data requirements and provide the necessary infrastructure to support their work
• Optimizing performance of data processing systems by tuning database queries, improving data access times, and reducing latency
• Building APIs and data ingestion processes to enable seamless data access across the organization
• Managing relationships with stakeholders across the organization, including business users, product managers, and other technical teams
• Keeping up to date with the latest technologies and tools in the data engineering field, and evaluating their potential use in the organization
• Maintaining engineering best practices and processes such as TDD, code reviews, code refactoring, branch management, and CI/CD practices to ensure code quality and reliability
• Collaborating with other engineers on areas such as DevOps, infrastructure, and security to ensure that data systems are properly integrated with the larger technology stack
• Supporting our technical lead with estimates and architectural decisions to ensure timely and regular delivery of features over time
• Developing and maintaining documentation for data processing systems, including technical specifications, data flow diagrams, and architecture diagrams

• 1. Apply via Otta
• 2. Video call with our Teck Team and Tech Lead
• 3. Take home assessment (~60minutes)
• 4. System design interview with the team
• 5. Culture fit interview with the CEO
• 6. Offer