Analytics Engineer

Brilliant Corners•Los Angeles, CA

1d•$95,000 - $105,000•Hybrid

About The Position

The Analytics Engineer is responsible for data consolidation and maintenance across the organization’s data infrastructure and various data sources. The engineer works with Data Analysts and other business units to create data models and to build and maintain a cloud data infrastructure. Their work will support recurring and ad-hoc reporting, dashboard creation and maintenance, and data requests from internal and external stakeholders. The Analytics Engineer will also build business intelligence tools to help data analysts do their work more efficiently, such as improved or automated data clean up processes and a self-service analytics platform. They will also collaborate with the Product Development team and IT Operations team on data infrastructure and integration projects focused on transforming data and supplying data between operational systems to meet business needs. Lastly, they are responsible for using best practices to maintain version control and perform internal testing and deploy production-ready code.

Requirements

Proven success in designing, building and maintaining data warehouse and infrastructure that meet business needs.
Experience with SQL.
Experience with Python or R.
Strong problem-solving skills in relation to data and software applications.
Excellent interpersonal skills and verbal/written/virtual communication skills to communicate technical details to both other team members and non-technical stakeholders.
Experience supporting and working with cross-functional teams.
Prior experience with software development best practices around code (version control using Git, testing, debugging, continuous integration and deployment, clear comments in code), especially implementing such practices in organizations.
Experience with cloud data warehouses and platforms like Snowflake, GCP, Azure.
Experience with ETL/ELT tools like FiveTran, Matillion, Stitch.
Experience with DBT, Dataform, Databricks.
Experience with data visualization tools like Tableau, PowerBI, Looker, Sigma.
Experience with Integration Platform-as-a-Service like Workato or Mulesoft.
Clear technical writing experience, including data mapping documents, reports, and presentations.
Experience in a non-profit, human services setting.
Candidates should have physical mobility for tasks such as standing, bending, stooping, kneeling, crouching, reaching, twisting, and walking on uneven surfaces.
They should be capable of performing stationary tasks like sitting for up to 6 to 8 hours a day.
Additionally, candidates should be able to lift, carry, push, pull light to moderate weights up to 15 pounds safely.
Requires mental acuity for analytical reasoning and document interpretation.

Responsibilities

Design, develop, implement, and maintain data models used for reporting and analytics.
Work with Data Analysts and other business teams to identify and develop ETL/ELT processes to automate, orchestrate, and maintain data pipelines.
Maintain data documentation and definitions, including data quality standards and business logic for metrics/KPIs, naming and formatting conventions and detailed specifications/documentation for analytics code.
Maintain version control for, test, and continuously integrate analytics code into testing and production environments.
Monitor data models and pipelines to ensure performance meets internal service level agreement.
Collaborate with Data Analysts to build dashboards and visualizations that show trends, patterns, and outliers in the data pipeline and models. As well as, maintain current inventory of reports and dashboards and publish this information to business users.
Work with Product Development and other business teams to consolidate and integrate data from and between multiple data sources (such as Salesforce, Financial Systems, HRIS, and other data sources).
Maintain and update (as necessary) existing integration infrastructure, with a focus on streamlining and consolidating processes.
Help evaluate and validate new and existing tools or development needs to suggest improvements and streamline operations.
Support updates/upgrades, configuration and troubleshooting for business intelligence tools.
Help coach Data Analysts on software development best practices.