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# Analytics Engineer

- **Company:** Databricks
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Business Logic, Business Intelligence Development, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Transformation, Data Systems, Performance Tuning, Standard Sql, SQL Databases, Data Layers, Pyspark, Databricks
- **Published:** October 17, 2026
- **Apply:** https://computerjobs.com/us/en/mob/job/E25708BB96F8B4CE69

## About the Role

* Significant experience working as an Analytics Engineer or in a closely related data engineering role.
 * Expert-level SQL skills and strong hands-on experience with PySpark.
 * Proven experience working with Databricks and Lakehouse architectures.
 * Strong understanding of dimensional modelling, including fact tables, dimension tables, star schemas and semantic layers.
 * Experience developing curated, analytics-ready datasets for Business Intelligence and analytics consumption.
 * Ability to translate complex business requirements into well-structured data models and consistent KPI definitions.
 * Strong understanding of data quality, performance optimisation and reliable data transformation practices.
 * Experience providing technical leadership, reviewing designs or mentoring other engineers.

Remote (ad-hoc travel to client HQ)

## Description

* Lead the design and development of curated, analytics-ready dimensional and semantic data models.
 * Build, maintain and optimise complex SQL and PySpark transformation pipelines within Databricks.
 * Define and maintain KPI definitions, conformed dimensions and consistent business logic across data domains.
 * Ensure data quality, integrity, testing, observability and performance across the curated data layer.
 * Lead modelling design reviews and contribute to technical and architectural decisions.
 * Work closely with Data Architects, BI Developers, analysts and business stakeholders to translate requirements into effective data solutions.
 * Establish and promote engineering standards, data governance practices and reusable modelling patterns.
 * Contribute to CI/CD pipelines, automated testing frameworks and continuous improvement across the data platform.