Job Description

About us

One team. Global challenges. Infinite opportunities. At Viasat, we're on a mission to deliver connections with the capacity to change the world. For more than 35 years, Viasat has helped shape how consumers, businesses, governments and militaries around the globe communicate. We're looking for people who think big, act fearlessly, and create an inclusive environment that drives positive impact to join our team.

What you'll do

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.

What are the real-world implications of your work?

Our work directly impacts Viasat's bottom line, currently generating significant incremental contracted revenues. The data products we build help sales teams identify the right prospects faster and close deals more efficiently cross BU.

When we integrate new tools into sales workflows or deploy AI-driven lead identification, it translates into shorter sales cycles and higher win rates. This means sales professionals spend less time on manual research and more time focused on high-value activities that drive results.
Our tools also improve how Viasat's partner organizations operate, extending our reach and effectiveness across the broader sales ecosystem

Who other types of people will the candidate have the opportunity to work with at Viasat?

The candidate will collaborate most closely with their immediate Data Led Sales team of 5, mentoring team members on analytics engineering best practices. They'll work extensively with the wider Growth Analytics team to ensure alignment on data modeling standards and shared analytical frameworks.
Regular collaboration with centralized data teams includes the Data Engineering team for infrastructure integration and pipeline orchestration.
The candidate will join strategic discussions with commercially focused teams including Sales Operations and Market Intelligence teams. These interactions provide crucial business context for data modeling decisions and help translate technical capabilities into commercial opportunities.
The hybrid nature of the role (2 days in London office) ensures regular face-to-face collaboration with both analytics colleagues and commercial stakeholders while maintaining flexibility for focused technical development work.

The day-to-day

The candidate will regularly monitor data pipeline health and quality metrics to ensure our sales tools have reliable, accurate data. They'll spend time 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.
They'll 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. The role involves frequent switching between hands-on technical work (writing dbt transformations, optimizing BigQuery performance) and discussions about data requirements for new sales applications.
Weekly activities include building ELT pipelines, mentoring team members on analytics engineering best practices, and architectural discussions with the Data Engineering team. They'll spend significant time in 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.

What you'll need

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)

What will help you on the job

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

EEO Statement

Viasat is proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, ancestry, physical or mental disability, medical condition, marital status, genetics, age, or veteran status or any other applicable legally protected status or characteristic. If you would like to request an accommodation on the basis of disability for completing this on-line application, please click here.