Best suited to candidates with strong technical expertise in data modeling and BI tools, working in enterprise environments that value collaboration across business functions and data teams.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from DDN via Ashby
The Role
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
Source: DDN careers (Ashby)