Senior Analytics Engineer

Viasat, Inc.•Carlsbad, CA

About The Position

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. As a Senior Analytics Engineer, you will own and extend our demand forecasting platform, modeling satellite bandwidth requirements across Maritime, Aviation, Enterprise, and emerging Direct-to-Device (D2D) markets. You'll translate executive-level business questions into scalable data pipelines and geospatial models, driving iterative cycles of data interpretation and assumption refinement. You'll turn ambiguous forecasting challenges into production-quality analytical tools—and just as importantly, help senior leadership understand what the data is (and isn't) telling them. Reporting to the Director, Commercial Business Analytics, you'll operate at the intersection of business strategy and data engineering—close enough to executive stakeholders to shape the why behind the models, and close enough to engineering to ensure your work can be productionized and scaled. Your forecasting outputs feed directly into capacity feasibility analyses run by engineering teams, making the handoff relationship critical.

Requirements

Bachelor's degree in a quantitative field
5–8 years of experience in analytics engineering, data engineering, or quantitative analysis
Demonstrated experience managing executive and cross-functional stakeholder relationships—translating complex analytical outputs into actionable business guidance
Hands-on GIS and geospatial analysis experience (e.g., H3, PostGIS, GeoPandas, Kepler.gl, or equivalent tooling)
Strong SQL and cloud data warehouse experience (BigQuery preferred)
Python proficiency with data engineering and analytical libraries (pandas, scipy, etc.)
Proven ability to structure ambiguous business problems into well-defined analytical frameworks
Experience building maintainable, scalable data pipelines with clear documentation and lineage
Strong written and verbal communication skills, including experience presenting to senior leadership
Comfort owning outcomes in fast-paced, evolving environments with minimal supervision

Nice To Haves

Master's degree or equivalent experience in a quantitative field
Experience transitioning analytical models into production infrastructure alongside engineering teams
Satellite or telecom industry experience
Hands-on experience with Dagster, Airflow, or dbt
Demand forecasting or capacity planning experience
Familiarity with spatial statistics, coverage modeling, or network planning tools

Responsibilities

Partner directly with business unit executives and C-level stakeholders to translate strategic forecasting needs into data models, proactively surfacing insights and challenging assumptions
Own and evolve demand forecasting pipelines using Python, SQL, and modern orchestration tools (Dagster, dbt, BigQuery)
Develop and extend geospatial demand models using GIS tooling (H3, PostGIS, Kepler.gl, GeoPandas) to translate global market opportunity into demand projections for targeted geographies
Drive forecasting model expansion to new business units and services through configurable, testable code—often where no existing baseline exists
Independently validate model outputs, refine assumptions, and recommend adjustments to input parameters based on observed patterns and business feedback
Interpret and present forecasting outputs in business context to senior leadership—identifying where model results challenge assumptions, surfacing data quality risks, and advising on methodology changes
Evaluate and integrate 3rd-party industry data to assess total global vertical demand and model geographic distribution
Build data structures with engineering handoff in mind—balancing analytical flexibility with the conventions and standards that enable smooth transition to production systems
Document methodologies, assumptions, and maintain clear data lineage across all forecasting workstreams
Deliver regional and scenario-based demand projections that directly inform capacity planning decisions
Collaborate with engineering teams who consume forecasting outputs, ensuring data formats, assumptions, and methodologies are well-documented and aligned with downstream systems
Continuously evaluate existing forecasting processes and recommend improvements to enhance accuracy, scalability, and stakeholder confidence