About the role

You will own the transformation and modeling layer of the enterprise data platform by building and maintaining dbt models. Additionally, you will partner with business stakeholders to translate analytical needs into scalable data models and build BI content for self-serve access.

What they look for

SQL Dbt Data Modeling Python Sigma Computing Looker BigQuery Snowflake Redshift Data Quality Dimensional Modeling Data Warehousing CI/CD Data Governance Business Intelligence Communication

Requirements

The role requires 5+ years of experience in analytics or data engineering with expert-level SQL and dbt proficiency. Candidates should have hands-on experience with cloud data warehouses and a strong understanding of dimensional modeling and data quality practices.

Full description

We’re looking for a Senior Analytics Engineer to own the transformation and modeling layer of DDN’s enterprise data platform. You’ll turn raw data from Salesforce, Workday, product systems, and other sources into trusted, well-documented datasets that stakeholders across Sales, Finance, Product, and Operations actually use to make decisions. You’ll work closely with data engineers who manage ingestion and infrastructure, and with analysts and business partners who consume what you build.

What You’ll Own

Data modeling — design, build, and maintain dbt models that transform raw data into clean, reliable datasets for analytics and reporting

Data quality — implement and maintain testing, monitoring, and documentation so stakeholders can trust what they’re looking at

BI & semantic layer — build and maintain Sigma data models and workbooks that give business users self-serve access to data

Collaboration — partner with business stakeholders to understand their analytical needs and translate them into scalable, maintainable data models; work with data engineers on source data requirements

Your Experience Includes

5+ years in analytics engineering, data engineering, or a similar data-focused role

Expert-level SQL including experience with complex joins, window functions, CTEs, and performance tuning

Strong experience with dbt for building and maintaining transformation pipelines

Hands-on experience with a cloud data warehouse (BigQuery, Snowflake, Redshift, or similar)

Understanding of dimensional modeling and data warehouse design patterns

Experience building and maintaining BI content (dashboards, data models, semantic layers) in tools like Sigma, Looker, or similar

Strong Python skills for data analysis, automation, or pipeline work

Solid understanding of data quality practices including testing, monitoring, documentation

Strong communication skills and demonstrated ability to translate technical concepts for business stakeholders

Bachelor’s degree in a quantitative field or equivalent practical experience

Nice to Have

Experience with Sigma Computing specifically

Familiarity with data quality frameworks (e.g., Elementary, Great Expectations)

Familiarity with data governance practices — PII handling, access controls, documentation standards

Experience with version-controlled, CI/CD-driven analytics workflows

Prior experience in a small data team where you wore multiple hats