Analytics Engineer

Viasat, Inc.•Carlsbad, CA

About The Position

As an Analytics Engineer, you will build and extend our demand forecasting platform, modeling satellite bandwidth requirements across Maritime, Aviation, Enterprise, and emerging Direct-to-Device (D2D) markets. You'll translate business questions into scalable data pipelines, working through iterative cycles of data interpretation and assumption refinement. You'll turn ambiguous forecasting challenges into production-quality analytical tools—and just as importantly, help stakeholders 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 internal customers to understand 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
3–5 years of experience in analytics or data engineering
Experience interpreting analytical outputs for business audiences—not just building models, but explaining what the results mean and where assumptions may need revisiting
Strong SQL and cloud warehouse experience
Python proficiency (pandas, numpy)
Ability to structure ambiguous business problems
Experience building maintainable, scalable data pipelines
Strong communication skills
Comfort in fast paced environments

Nice To Haves

Master’s degree or equivalent experience in a quantitative field
Experience working alongside engineering teams to transition analytical models into production infrastructure
Satellite, telecom, or aviation industry experience
Familiarity with Dagster, Airflow, and dbt
Demand forecasting or capacity planning experience
Geospatial data exposure (H3, Kepler.gl)
Experience transitioning analytics into production systems

Responsibilities

Build and maintain demand forecasting pipelines using Python, SQL, and modern orchestration tools (Dagster, dbt, BigQuery)
Extend forecasting models to new business units and services through configurable, testable code – in many cases where no existing baseline exists
Partner with Business Unit leaders to translate forecasting needs into data models
Build data structures with engineering handoff in mind—balancing analytical flexibility with the conventions and standards that enable smooth transition to production systems
Validate model outputs and refine assumptions based on business feedback
Interpret forecasting outputs in business context—identifying where model results challenge assumptions, surfacing data quality issues, and recommending adjustments to input parameters based on observed patterns
Work with 3rd party industry data to understand total global vertical demand and translate this into demand in targeted geographies
Document methodologies, assumptions and maintain clear data lineage
Deliver regional and scenario-based demand projections
Collaborate with engineering teams who consume forecasting outputs for capacity planning, ensuring data formats, assumptions, and methodologies are well-documented and aligned with downstream systems
Work closely with colleagues to develop forecasting requirements, pressure-test assumptions, and evolve models as the business uncovers new questions