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.
What you bring to the role
Basic Qualifications:
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.
Responsibilities
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.
What you bring to the role
Basic Qualifications:
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.
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