Original listing text, shown exactly as published by the company.
Your Mission
Data Engineers at Datatonic work across a wide range of projects, helping clients unlock the full potential of the Modern Data Stack. You’ll bring expertise in our technologies of choice - dbt, Looker, Snowflake, BigQuery, Google Cloud, Sigma, Fivetran, Python, Spark, Pub/Sub - and apply them to solve real client challenges.
In this role, you’ll collaborate closely with a Delivery Manager, support project teams, and make hands-on contributions to the codebase where needed. Our Data Engineers combine strong technical skills with a client-focused mindset, ensuring that data solutions are not only well-engineered but also deliver measurable impact.
What You’ll Do
- Foundational Support for Analytics and Data Science Teams: Build the infrastructure that enables analytics and data science teams to deliver innovative, impactful solutions for clients.
- Google Cloud Migration and Data Warehouse Solutions: Assist clients in migrating their existing business intelligence and data warehouse solutions to Google Cloud.
- Build Scalable Data Pipelines: Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications.
- Design and Build Data Warehouses and Data Marts: Design and implement new data warehouse and data mart solutions, including:
- Transforming, testing, deploying, and documenting data.
- Understanding data modeling techniques.
- Optimising and storing data for warehouse technologies.
- Manage Cloud Infrastructure: Architect, maintain, and troubleshoot cloud-based infrastructure to ensure high availability and performance.
- Collaboration with Technology Partners: Work closely with technology partners such as Google Cloud, Snowflake, dbt, and Looker, mastering their technologies and building a network with their engineers.
- Agile and Dynamic Team Collaboration: Collaborate in an agile and dynamic environment with a team of data engineers, BI analysts, data scientists, and machine learning experts.
- Applying Software Engineering Best Practices: Implement software engineering best practices to analytics processes, such as version control, testing, and continuous integration.
What You’ll Bring
- Experience: 4+ years in a data-related role (e.g., Data Engineer, Data Analyst, Analytics Engineer).
- Technical Expertise: Hands-on experience with Looker, dbt, modern data warehouses like Snowflake or BigQuery, and Kimball data modeling.
- Strong Programming Skills: Expertise in Python and/or Java, with proficiency in SQL.
- Experience in Data Engineering: 5+ years of experience in designing and building scalable data solutions.
- High-Quality Code Standards: Ability to write tested, resilient, and well-documented code.
- Cloud Computing Experience: Experience in building and maintaining cloud infrastructure (GCP or AWS is a plus).
- Problem-Solving Mindset: Ability to take ownership and drive projects from concept to completion.
- Project Management: Natural ability to manage multiple initiatives and clients simultaneously.
- SQL Proficiency: Skilled in writing analytical SQL, with an understanding of the difference between SQL that works and performant SQL.
- Business Translation: Experience in translating business requirements into technical solutions.
- Communication Skills: Ability to communicate complex ideas simply to a wide range of audiences.
- Leadership: Experience in providing technical guidance and direction on projects.
- Cultural Alignment: Complete alignment with our culture of transparency, empathy, accountability, and performance.
Bonus points if you have
- dbt Developer certification
- Google Cloud Professional Data Engineer certification
- Snowflake SnowPro certification
- Experience with Scrum methodology
- Client-Facing Role: Prior experience in a client-facing position
- API Development Experience: Experience building scalable REST APIs using Python or similar technologies.
What’s in It for You?
