Analytics Engineer
About The Position
Our team builds data-driven products that directly drive Viasat's revenue growth by transforming how our sales teams operate. We create innovative data and software tools that sales teams use to close more deals and identify new opportunities. As the main data owner for our 5-person team, the candidate will architect and own the complete data infrastructure that powers these revenue-generating tools. They'll have the unique opportunity to work at the intersection of advanced analytics engineering, product strategy, and commercial impact. This role involves designing scalable data pipelines, building robust data models, and creating the technical foundation that enables our tools to process massive datasets and deliver real-time insights to sales teams. The candidate will lead technical architecture decisions while partnering closely with stakeholders to translate complex sales requirements into scalable data solutions. They'll mentor team members on analytics engineering best practices, ensuring our rapidly growing suite of tools maintains enterprise-grade performance and reliability. This role offers direct exposure to how technical data architecture decisions translate into measurable revenue impact - seeing their pipeline optimizations directly improve sales team productivity and deal closure rates. With our tools already generating significant incremental revenues and being "well-loved" by sales teams, this is an opportunity to scale proven solutions from successful prototypes to enterprise-wide platforms that transform how Viasat uses data strategically across the organization.
Requirements
- Strong SQL skills for large-scale data transformations
- Strong Python skills for data pipeline development
- Experience with dbt and dbt Cloud for building and orchestrating data pipelines
- Experience with GCP (particularly BigQuery)
- Experience with Terraform for infrastructure as code
- Strong hands-on experience with Git for version control
- Experience with data modeling concepts (dimensional modeling, star schemas)
- Experience with data quality and testing tools (dbt tests, Great Expectations)
Nice To Haves
- Architectural thinking - ability to design scalable data solutions that can grow from current needs to enterprise-wide deployment
- Proactive problem-solving approach - identifying data quality issues and optimization opportunities before they impact business users
- Strong communication skills with non-technical stakeholders to understand business requirements and translate them into technical solutions
- Mentoring and knowledge-sharing abilities - willingness to teach analytics engineering best practices and upskill team members
- Experience with CI/CD pipelines for data transformations to support our automated deployment processes
- Understanding of data warehouse design principles and best practices for enterprise-scale architecture
- Experience with geospatial analytics using BigQuery GIS or similar tools for location-based analytics
- Data visualization experience with tools like Tableau to understand end-user requirements for data models
- Advanced analytical modeling experience - statistical analysis and predictive modeling on large-scale datasets
- Data governance and metadata management knowledge - understanding of data lineage, cataloging, and enterprise data management practices
- Experience with modern data stack tools (Airbyte, Fivetran, etc.) as the team scales integrations
- Continuous learning mindset - staying current with evolving analytics engineering practices as our team scales from startup-style rapid development to enterprise-grade solutions
Responsibilities
- Regularly monitor data pipeline health and quality metrics to ensure our sales tools have reliable, accurate data.
- Coding in SQL and Python to build data transformations, optimize complex queries processing large customer datasets, and create data models that power custom applications, websites, analytical dashboards, and automated lead generation systems.
- Conduct stakeholder meetings with the Data Led Sales team on new data architecture designs and collaborate with the Growth Analytics team on shared modeling standards.
- Building ELT pipelines
- Mentoring team members on analytics engineering best practices
- Architectural discussions with the Data Engineering team.
- Code reviews, ensuring data quality through automated testing frameworks, and documenting data models so other teams can effectively build applications and conduct analysis using their work.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Industry
Telecommunications
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
