About the Team:

The Academic Information Systems (AIS) DataOps team is a shared technology group responsible for building, administering, governing, and setting standards for a growing number of strategic data platforms and services. Our capabilities enable data to be extracted, centralized, transformed, transmitted, and analyzed across a range of products in the AIS space. Due to our footprint across the enterprise, we are relied upon to ensure our systems are trusted, reliable and available. The technology underpinning these capabilities includes industry leading data and analytics products such as Snowflake, Astronomer/Airflow, Kubernetes, DBT, Tableau, Sisense, Collibra, and Kafka/Debezium. Our mission is to enable frictionless experiences for our AIS colleagues and customers so that they can openly and securely consume trustworthy data, enhancing everyday interactions and decisions.

About the role:

As a Senior Data Engineer I, you will play a key role in building secure, scalable, and reliable data infrastructure that supports our global operations. You will collaborate across teams to facilitate the smooth flow of data, ensuring high-quality information is available for analysis and decision-making. Your work will help drive impactful solutions for colleagues and customers by enabling effective data sharing and storytelling. 

Responsibilities: 

• Design and implement robust data orchestration and transformation solutions. 

• Ensure reliable delivery of high-quality data for analysis and sharing. 

• Collaborate across teams to establish and promote coding and technical standards. 

• Work with DevOps to automate deployments and use Infrastructure as Code (IaC) for consistent environments. 

• Develop reusable frameworks, modular components, and common patterns for efficient and reliable deployment using Airflow. 

• Document and share best practices, fostering knowledge exchange among teams. 

• Enhance platform reliability and scalability through improved logging, monitoring, and observability. 

• Identify and address data platform gaps, support DataOps initiatives, and incorporate user feedback. 

Requirements: 

• Experience with modern data stack technologies such as Airflow, Snowflake, and DBT. Relevant Tableau, Sisense, AWS, GitHub, Terraform, and/or Docker experience is also highly desirable. 

• Ability to create deployable data pipelines and ETL/ELT solutions using Python, SQL, or Jinja. 

• Knowledge of Software Development Life Cycle (SDLC), DataOps, and DevOps practices. 

• Active participation in Agile environments and willingness to continuously improve. 

• Strong communication and collaboration skills; openness to feedback and new ideas. 

• Familiarity with designing secure, scalable, and cost-effective cloud-based data solutions. 

• Understanding of data governance, privacy, and security practices.