Staff Analytics Engineer

Coursera Coursera · Consumer · United States · Data Science

Staff Analytics Engineer at Coursera, focusing on building robust data pipelines and models to support analytics, AI, and machine learning initiatives. The role involves architecting data pipelines, leading data modeling strategies, and driving data governance. Collaboration with data scientists and stakeholders is key, with an emphasis on developing self-serve analytics products and incorporating AI-driven capabilities into data frameworks. Experience with data lake architecture, batch/streaming, and data observability is required.

What you'd actually do

  1. Architect scalable data models and construct high quality ELT pipelines that act as the backbone of our core data lake, with cutting edge technologies such as Airflow, DBT, Databricks, and Sigma. Your work innovates with principles adopted by others.
  2. Design, build, and launch self-serve analytics products from data consumption to data discovery and enablement. Your creations reach far beyond basic dashboarding and are intimately tied with business outcomes, identifying root causes of trends that have immediate impact.
  3. Be a technical leader for the team. Your proficiency in technical and architectural designs for major team initiatives will inspire others. Help shape the future of Analytics Engineering at Coursera and foster a culture of continuous learning and growth.
  4. Be a data leader for the business. Your initiatives will directly increase data literacy, significantly reduce pain points, and resolve data gaps.
  5. Partner with data scientists, business stakeholders, and product engineers to define, curate, and govern high-fidelity data. Your ability to see KPI interrelationships and how they maximize ROI across the business makes you a recognized bridge connecting data and business outcomes.
  6. Develop new tools and frameworks in collaboration with other engineers. Your innovative solutions will enable our customers to understand and access data more efficiently, while enhancing frameworks with AI-driven capabilities.

Skills

Required

  • data architecture
  • data pipelines
  • reporting
  • relational databases
  • DRY data modeling practices
  • efficient SQL code generation
  • AWS
  • Databricks
  • Delta Lake
  • Airflow
  • dbt
  • Redshift
  • Datahub
  • BI Tools
  • Looker
  • Sigma
  • Data Observability frameworks
  • Monte Carlo
  • Great Expectations
  • AI tools
  • Claude
  • Gemini
  • Cursor
  • data lake architecture
  • batch and streaming architectures
  • data governance
  • technical best practices

Nice to have

  • Databricks preferred
  • dbt preferred
  • Looker preferred
  • Sigma preferred

What the JD emphasized

  • 10+ years experience in data/analytics engineering with expertise in data architecture, pipelines, and reporting. Expert experience with relational databases, DRY data modeling practices, and efficient SQL code generation
  • Expert experience with some of: AWS, Databricks, Delta Lake, Airflow, dbt, Redshift, Datahub; Databricks and dbt preferred
  • Expert experience with crafting and driving self service reporting solutions with hands on experience in BI Tools; Looker or Sigma preferred
  • Strong experience implementing Data Observability frameworks (e.g., Monte Carlo, Great Expectations) at an enterprise level
  • Strong hands on experience with AI tools such as Claude, Gemini, Cursor and its role in streamlining data processing and enabling data democratization
  • Strong experience with data lake architecture and batch and streaming architectures
  • Strong experience in driving industry standards in data governance and technical best practices and driving standards across multiple engineering pods or business disciplines