SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Coursera and Udemy have merged to create a comprehensive skills platform serving 300+ million learners and 12,000+ enterprise customers. As a Staff Analytics Engineer, you will architect scalable data pipelines and models that power business-critical applications, AI/ML initiatives, and external-facing data products driving revenue and customer satisfaction.
You will lead enterprise-wide data modeling strategies, establish data governance standards, and drive a culture of transparent, well-governed data systems. Your responsibilities include architecting high-quality ELT pipelines using modern technologies (Airflow, dbt, Databricks, Sigma), designing and launching self-serve analytics products that connect directly to business outcomes, and serving as a technical leader who inspires the team and shapes the future of analytics engineering at the company.
You will partner with data scientists, business stakeholders, and product engineers to define and govern high-fidelity data, acting as a bridge between data and business outcomes. Your work will increase data literacy across the organization, reduce pain points, and resolve critical data gaps. You'll develop innovative tools and frameworks that enable customers and internal teams to understand and access data more efficiently, leveraging AI-driven capabilities.
Required qualifications include 10+ years in data/analytics engineering with expertise in data architecture, pipelines, and reporting. You need expert-level proficiency with relational databases, SQL, and DRY data modeling practices. Experience with AWS, Databricks, Delta Lake, Airflow, and dbt is essential (dbt required, Databricks preferred). You should have hands-on experience building self-service reporting solutions with BI tools like Looker or Sigma, strong root cause analysis skills, and demonstrated experience implementing enterprise-level data observability frameworks. Experience with AI tools (Claude, Gemini, Cursor) and data lake architecture (batch and streaming) is required. You must have a proven track record driving industry standards in data governance and technical best practices across multiple teams.
