Staff Analytics Engineer

About this role

About Coursera + Udemy

Coursera and Udemy are now one company, bringing together two mission-driven brands to create the world’s most powerful platform for turning learning into progress. Together, we help more than 300 million learners and 12,000+ enterprise customers build the skills they need for a world being reshaped by AI. Read more about the combined company by visiting our blog .

Why join us now?

AI is transforming how people learn, work, and grow, and the need for new skills has never been greater. Coursera brings trusted content and credentials from leading university and industry partners, while Udemy brings a dynamic skills marketplace and global network of real-world experts. By combining these strengths, we can connect more people and organizations to the skills they need, when they need them.

Shape what comes next

By joining our team, you’ll have the opportunity to reshape how the world learns and applies skills—and help millions of people participate in the new economy. Bring your ideas, expertise, and perspective to meaningful work that can make a difference at global scale.

Job Overview:

Analytics Engineering plays a crucial role in building robust and reliable data pipelines and data models that enable data-driven decision-making, powering various analytics, AI, and machine learning initiatives within Coursera. In addition, Analytics Engineering today owns many external facing data products that drive revenue and boost partner and learner satisfaction.

As a Staff Analytics Engineer, you will be architecting high quality and scalable data pipelines powering business critical applications, leading enterprise wide data modeling strategies and driving a culture of transparent, well-governed data systems. You will collaborate with both technical and cross-functional leaders to lead and set direction on how we craft and look at data, while driving industry accepted standards on data governance, discoverability, and accessibility. You will craft technical decisions and design trade-offs to ensure we can speedily deliver on ambitious, innovative goals while building the foundations for extension and scale in years to come.

Responsibilities:

• 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.

• 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.

• 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.

• Be a data leader for the business. Your initiatives will directly increase data literacy, significantly reduce pain points, and resolve data gaps.

• 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.

• 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.

Basic Qualifications:

• 10+ years experience in data/analytics engineering with expertise in data architecture, pipelines, and reportingExpert 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 preferred, dbt required

• Expert experience with crafting and driving self service reporting solutions with hands on experience in BI Tools; Looker or Sigma preferred

• Strong understanding and demonstrated experience in root cause analysis, with a background in Data Science or Business a plus

• 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

• Strong ability to communicate technical concepts clearly and concisely to leadership

• Proven relationships with business end users with clear understanding of how data is used to power business decisions with demonstrated storytelling skills connecting data with trends observed in business

• Independence and passion for innovation and learning new technologies; seeks out and creates high-impact projects

Preferred Qualifications:

• Strong Experience leading cross-functional RFCs (Request for Comments) and driving technical standards across multiple engineering pods or business disciplines

• Strong experience with data lake architecture and batch and streaming architectures

• Proven track record building feature stores or data pipelines specifically for LLM fine-tuning and RAG architectures

• Strong track record of leadership and mentorship in elevating data culture, preferably in a remote environment

Keep Learning

If this opportunity interests you, you might like these courses on Coursera:

• Big Data Specialization

• Generative AI Fundamentals

• Data Warehousing for Business Intelligence

Compensation / Location:

At

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