Senior Data Engineer

Senior Data Engineer

Data and 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 product features within Coursera. In addition, Data and Analytics Engineering today owns many external facing data products embedded into our products that drive revenue and boost partner and learner satisfaction.

As a Senior Data Engineer, you will be building 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 stakeholders to lead and set direction on how we craft and look at data, while following industry accepted standards on data engineering. 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:

• Build 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 data visualization tools like Sigma, Looker and Tableau.
• Build data pipelines and launch self-serve analytics products from data consumption to data discovery and enablement. Your creations are intimately tied with business outcomes and identifying root causes of trends that have immediate impact.
• Be a mentor to your team and advocate for the success of your stakeholders. Your initiatives will directly increase data literacy, significantly reduce pain points of the business, 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 contribute to our data platform 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:

• 8+ years experience in data/analytics engineering with expertise in data architecture, pipelines, and reportingStrong experience with relational databases, DRY data modeling practices, and efficient SQL code generation
• Strong experience with some of: AWS, Databricks, Delta Lake, Airflow, dbt, Redshift, Datahub; Databricks and dbt preferred
• Well versed and 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, quality and monitoring frameworks/tools (e.g., dbt tests, Great Expectations, Datadog etc.) 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 like handling PII data
• 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 working with cross-functional teams like product/backend engineering pods or business analytics teams and translating ambiguous business requirements into robust, durable data solutions
• Databricks native experience using Pyspark and Spark SQL and other tools like databricks workflows with advanced dbt concepts like unit/data tests, incremental and idempotent modeling
• Strong track record of mentorship in elevating data culture, preferably in a remote environment