InStride · 3 days ago

Analytics Engineer

Los Angeles, CA or Remote

Full-time

Remote

Mid, Senior Level

$130K/yr - $150K/yr

5+ years exp

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Responsibilities

Design, build, and optimize the gold layer in Databricks, creating dimensional models that support business reporting and analytics.

Ingest data from various sources, including non-application data sources, using SQL and Python to clean, transform, and load data.

Work closely with business leaders, analysts, and data engineers to understand data requirements, define key business metrics, and develop data models that meet the needs of various stakeholders.

Build and maintain data models and Tableau dashboards, enabling stakeholders to self-serve insights and make data-driven decisions.

Monitor and optimize ETL processes and data models for performance, ensuring timely access to key data sets and insights.

Maintain comprehensive documentation of data models, pipelines, and business rules.

Collaborate with data analysts and engineers to enable more advanced analytics use cases, such as predictive modeling or trend analysis.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Dimensional Data ModelingData IngestionSQLPythonTableauData SecurityPerformance OptimizationAdvanced AnalyticsDocumentation

Required

5+ years of experience in data engineering or a highly technical analytics role, where you worked extensively with data modeling and business intelligence tools.

5+ years of experience working in a fast-paced environment.

Deep understanding of data security, data modeling, Python, SQL, and BI tools like Tableau.

Experience ingesting data from a variety of sources, including non-application data sources.

Ability to design and implement robust, scalable data structures that support BI tools and deliver meaningful insights.

Experience building and maintaining data models and Tableau dashboards.

Ability to monitor and optimize ETL processes and data models for performance.

Experience maintaining comprehensive documentation of data models, pipelines, and business rules.

Proactive approach to seeking out new ways to improve processes, tools, and models.