Senior Data Engineer – Snowflake & dbt

About the role

Sparq is looking for a Senior Data Engineer with deep expertise in Snowflake and dbt to design, build, and optimise large‑scale data pipelines. You will work on enterprise‑level datasets, ensuring high performance, cost efficiency, and robust data models while collaborating with cross‑functional teams.

Key responsibilities

• Design and implement scalable data pipelines in Snowflake and dbt that handle billions of rows.
• Optimise Snowflake storage, compute, and query execution through clustering, partitioning, indexing, materialised views and workload management.
• Migrate and refactor legacy data processes using dbt, improving transformation quality and performance.
• Collaborate with business and data stakeholders to translate requirements into high‑performance solutions.
• Leverage AI tools such as Claude to automate tasks and enhance workflow efficiency.
• Troubleshoot and resolve bottlenecks in existing ETL/ELT workflows.
• Provide technical leadership, mentorship, and documentation for best‑practice data engineering.

Required profile

• 3+ years of data engineering experience with cloud‑based, enterprise‑scale solutions.
• Proven track record handling massive datasets (billions of rows) in Snowflake.
• Strong problem‑solving skills and ability to optimise inefficient queries.

Required skills

• Snowflake performance tuning, storage optimisation, cost management.
• dbt for data transformation, testing, and orchestration.
• SQL and Python for data manipulation and automation.
• Experience with clustering, partitioning, indexing, materialised views, and workload management.
• Familiarity with AI assistance tools (e.g., Claude/Anthropic).

What we offer

• Collaborative, diverse team environment.
• Continuous growth through training, mentorship, and certifications.
• Access to cutting‑edge technologies across multiple industries.
• Remote or hybrid work options and inclusive employee recognition programs.