Staff Analytics Engineer

• About Coursera
• Coursera and Udemy are now one company, creating one of the world's most comprehensive skills development platforms for the AI era. This strengthens our ability to accelerate AI-powered innovation and shape how the world discovers and builds skills at a pivotal moment of change. Read more about the combined company by visiting our blog .
• Coursera was launched in 2012 by Andrew Ng and Daphne Koller with a mission to provide universal access to world-class learning. Coursera partners with leading university and industry partners to offer a broad catalog of content and credentials, including courses, Specializations, Professional Certificates
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• Why Join Us
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• grow your career, apply below.
• 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
• 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
• driving industry accepted standards on data governance, discoverability
• 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
• 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
• product engineers to define, curate
• 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
• enhancing frameworks with AI-driven capabilities

Basic Qualifications

• 10+ years experience in data/analytics engineering with expertise in data architecture, pipelines
• reporting. Expert experience with relational databases, DRY data modeling practices
• 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
• 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 Coursera, we offer competitive, zone-based pay aligned to your location, experience
• role level. Our total rewards package goes beyond salary, with comprehensive health and wellness benefits, bonus and RSU equity programs
• global perks designed to help you grow and thrive wherever you are.
• This role is only available for hire in [insert location]
• US Zone 1: $188,000 - $235,000
• US Zone 2: $182,000 - $228,000
• US Zone 3: $167,000 - $209,000
• US Zone 4: $156,000 - $195,000
• US Pay Zones:
• US-Z1: Bay Area (within 75 miles)
• US-Z2: NYC and Seattle Metro (within 75 miles)
• US-Z3: CA, WA, NY, NJ, CO, CT, DC, GA, IL, MA, MD, OR, RI, TX, VA
• US-Z4: AK, AZ, DE, FL, HI, ID, IN, IA, KS, KY, MI, MN, MO, MT, NC, NV, NH, OH, OK, PA, SC, TN, UT, VT, WI
• #PL-1
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