Senior Analytics Engineer
At a glance
Senior Data Analysis & BI role at DDN. New York · full-time · $150,000–$185,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain dbt models that turn raw source data into dependable datasets for reporting and analysis
- Add tests, monitoring and documentation so stakeholders can assess the quality of the data they use
- Create Sigma models and workbooks that let business users explore data on their own
- Translate stakeholder questions into reusable data models, and coordinate source-data needs with data engineers
What you bring
- At least five years in analytics engineering, data engineering or a comparable data-focused role
- Strong SQL expertise, including complex joins, window functions, CTEs and query performance tuning
- Substantial hands-on dbt experience building and maintaining data transformations
- A quantitative degree or equivalent practical experience
Who this fits
This senior role suits someone who can build trustworthy analytics datasets and explain technical choices to business partners. It is a full-time hybrid position based in New York.
From the employer
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
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