Design, build, and maintain trusted, scalable, and well-documented data models and pipelines using modern analytics engineering tools. This role ensures that analysts and business leaders can extract insights efficiently and reliably from clean data layers.
Responsibilities
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Data Modeling & Transformation
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Develop and maintain dbt models with standardized naming, structure, and robust documentation.
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Create analytics-ready data marts by transforming raw data into clean, structured datasets.
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Write modular, well-tested, and efficient SQL code to power modeling layers.
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Data Pipeline Development
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Build reliable and scalable data pipelines for ingestion, transformation, and validation.
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Monitor and troubleshoot data pipeline performance, latency, and failures.
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Cross-functional Collaboration
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Work closely with analysts and business stakeholders to translate data requirements into technical solutions.
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Ensure data logic aligns with business definitions and operational context.
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Data Quality & Documentation
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Implement data validation checks and tests to ensure accuracy and trustworthiness.
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Maintain clear documentation of data models, transformation logic, and data lineage.
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Use Git for version control and collaboration best practices.
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Infrastructure Support
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Support data infrastructure maintenance in cloud environments (BigQuery, Redshift, Snowflake).
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Adapt to evolving infrastructure, tools, and processes in a fast-paced environment.
Requirements
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Bachelor’s degree in Engineering, Computer Science, or a related technical field.
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1–2 years of experience in analytics engineering, data engineering, or equivalent data-focused roles.
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Proficient in writing complex SQL queries.
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Experience with dbt or similar data modeling tools.
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Familiarity with Git (branching, commits, pull requests).
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Exposure to cloud data warehouses (e.g., BigQuery, Redshift, Snowflake).
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Understanding of data validation, quality testing, and documentation practices.
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Familiarity with cloud platforms (e.g., GCP, AWS) is a plus.