What You’ll Be Doing

• Develop and maintain ELT pipelines, ensuring data reliability and scalability for business reporting and analytics use cases.
• Build and optimize SQL-based data models using dbt and other ETL tools.
• Support ZAP’s progress toward a more AI-enabled operating model, using emerging technologies to help improve team productivity.
• Identify and implement improvements in data delivery, processing performance, and system efficiency.
• Collaborate with team members to define requirements and translate them into scalable data models and pipelines.
• Contribute to the team’s technical vision and bring innovative solutions to enhance data systems.
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Basic Qualifications

What you bring to the role

• 5+ years of data engineering experience building, maintaining and working with data pipelines & ETL processes in big data environments.
• Extensive experience with SQL, ideally in the context of data modeling and analysis.
• Hands-on production experience with dbt, and proven knowledge in modern and classic Data Modeling - Kimball, Inmon, etc.
• Programming skills in Python or a similar language, with an emphasis on data transformation and automation.
• Experience with cloud columnar databases (Google BigQuery, Amazon Redshift, Snowflake), query authoring (SQL) as well as working familiarity with a variety of databases.
• Proven experience in performance testing, capacity planning, and cost optimization for large-scale, complex data pipelines and systems. This includes identifying bottlenecks, ensuring scalability, and minimizing operational costs in cloud-based data environments.
• Excellent communication and collaboration skills.
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Preferred Qualifications

• Experience building or operating an AI-augmented engineering practice - agentic IDE workflows (Cursor, Claude Code), prompt/skill engineering, eval design, and the discipline of treating AI artefacts as production code.
• SnowPro Core certification or equivalent hands-on expertise.
• Hands-on production experience with Apache Spark (Spark SQL / PySpark).
• Familiarity with Lean/6 Sigma principles and an understanding of CRM analytics.
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Our Data Stack

ELT: Snowflake, dbt, Airflow, Kafka

BI: Zendesk proprietary application, Looker

• Infrastructure: AWS, Kubernetes, Terraform, GitHub Actions