Senior Data Engineer

Role Description

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.
• Driving a culture of transparent, well-governed data systems.
• Collaborating with both technical and cross-functional stakeholders to lead and set direction on data crafting.
• Crafting technical decisions and design trade-offs for ambitious, innovative goals.

Responsibilities

• Build scalable data models and construct high quality ELT pipelines using technologies such as Airflow, DBT, Databricks, and data visualization tools like Sigma, Looker, and Tableau.
• Launch self-serve analytics products from data consumption to data discovery and enablement.
• Mentor your team and advocate for the success of your stakeholders.
• Partner with data scientists, business stakeholders, and product engineers to define, curate, and govern high-fidelity data.
• Develop new tools and contribute to our data platform frameworks in collaboration with other engineers.

Qualifications

• 4+ years experience in data/analytics engineering with expertise in data architecture, pipelines, and reporting.
• Strong 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.
• 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.
• Databricks native experience using Pyspark and Spark SQL and other tools like Databricks workflows.
• Strong track record of 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