At a Glance
- Tasks: Design and build cutting-edge data infrastructure using Snowflake and dbt.
- Company: Avidity, a forward-thinking tech company focused on data innovation.
- Benefits: Remote-first work culture, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on modern engineering practices.
- Why this job: Shape the future of data engineering and make a real impact on company strategy.
- Qualifications: 3+ years in data engineering, strong SQL skills, and experience with Snowflake.
The predicted salary is between 63000 - 77000 £ per year.
Avidity is investing in the future of its data platform, and the Data Team within the CIO department is central to that ambition. We are looking for a Data Engineer to help us design, build and operate the next generation of our data infrastructure – a modern, cloud-agnostic platform built around Snowflake and dbt.
This is a genuine opportunity to shape direction rather than simply maintain what exists. You will join us at a pivotal point as we evolve our engineering practice toward a modular, ELT-first architecture. Working closely with analysts, data scientists and business stakeholders, you will build reliable, well-tested and well-documented data products that people across the organisation can trust and use with confidence.
Key Responsibilities
- Modern data modelling & transformation. Design, build and maintain robust, scalable data transformation pipelines in dbt, producing version-controlled, tested and documented data models on Snowflake.
- ELT pipeline development. Develop and maintain efficient ELT processes that ingest and integrate data from diverse internal and external sources, prioritising modularity, reusability and maintainability.
- Data warehousing. Model, structure and curate data in Snowflake to serve analytics, reporting and data science, applying dimensional and modern data-modelling best practice.
- Performance & cost optimisation. Tune SQL, dbt models and Snowflake warehouse usage to improve query performance, control compute cost and keep processing times low.
- Data quality & observability. Establish and maintain rigorous data-quality checks, dbt tests and monitoring so that issues are caught early and data can be trusted.
- Security & governance. Build data solutions that are secure by design, ensuring compliance with global privacy regulations (e.g. UK GDPR) and internal governance standards.
- Engineering practice. Apply software-engineering discipline to data – version control, code review, CI/CD and automated testing – following Agile ways of working.
- Collaboration & support. Work with cross-functional teams to understand business needs and deliver data solutions that meet them, and provide ongoing support and timely troubleshooting for pipelines in production.
- Document architecture, data flows and models so that colleagues and stakeholders have a clear, current understanding of our data platform.
Essential Skills & Experience
- 3+ years' experience in a data engineering role, building and optimising data pipelines and cloud data platforms.
- Strong, demonstrable command of SQL, including query profiling, optimisation and dynamic T-SQL.
- Hands-on experience with dbt (Core or Cloud) – building models, macros, incremental models, tests and packages.
- Solid understanding of data warehousing concepts and modern data-modelling practices. Practical experience with Snowflake is essential; strong equivalent cloud data-warehouse experience (e.g. BigQuery, Redshift, Databricks) with willingness to move to Snowflake will also be considered.
- Proficiency in Python for data engineering tasks (e.g. ingestion, orchestration, automation).
- Experience with version control (Git) and CI/CD, and a genuine software-engineering mindset applied to data.
- A degree in Computer Science, Software Engineering or a related field, or equivalent practical industry experience.
Desirable Skills
- Experience with the Azure data ecosystem (Azure Data Factory, Azure SQL Database, Microsoft Fabric).
- Familiarity with PySpark or distributed data processing.
- Experience with orchestration tooling (e.g. Airflow, Dagster, Prefect, or Azure DevOps pipelines).
- Exposure to data-catalogue, lineage or governance tooling.
What We're Looking For
- Strong problem-solving and analytical skills, with an eye for pragmatic, maintainable solutions.
- The ability to explain complex data concepts clearly to non-technical stakeholders.
- A collaborative approach – comfortable working with cross-functional teams and external vendors.
- Self-motivation and the ability to work both independently and as part of a team in a fast-paced, Agile environment.
- Curiosity and a willingness to help set the technical direction of the team rather than simply follow it.
Why Join Us?
- Shape the direction. You'll help lead our move to a modern Snowflake and dbt platform, influencing architecture and standards from the ground up.
- Modern tooling. We're committed to an ELT-first, engineering-led approach with the tools to match.
- Real impact. Your work will directly inform company strategy and decision-making.
- Remote-first and hybrid working options.
Data Engineer employer: McCurrach
As a Customer Development Executive, you will thrive in a dynamic work environment that values innovation and collaboration. Our company offers competitive salaries, comprehensive benefits, and ample opportunities for professional growth, all while fostering a supportive culture that encourages creativity and teamwork. Located in a vibrant area, we provide a unique chance to engage with local retailers and make a tangible impact on our brand's success.
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We think this is how you could land Data Engineer
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We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
