Senior Analytics Engineer
RigNet, Carlsbad, CA, United States
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
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.
The day-to-day
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 third 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
What you’ll need
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
What will help you on the job
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
Salary range
$85,500.00 - $135,000.00 / annually. For specific work locations within San Jose, the San Francisco Bay area and New York City metropolitan area, the base pay range for this role is $104,500.00- $156,500.00/ annually. At Viasat, we consider many factors when it comes to compensation, including the scope of the position as well as your background and experience. Base pay may vary depending on job-related knowledge, skills, and experience. Additional cash or stock incentives may be provided as part of the compensation package, in addition to a range of medical, financial, and/or other benefits, dependent on the position offered.
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.
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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
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.
The day-to-day
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 third 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
What you’ll need
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
What will help you on the job
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
Salary range
$85,500.00 - $135,000.00 / annually. For specific work locations within San Jose, the San Francisco Bay area and New York City metropolitan area, the base pay range for this role is $104,500.00- $156,500.00/ annually. At Viasat, we consider many factors when it comes to compensation, including the scope of the position as well as your background and experience. Base pay may vary depending on job-related knowledge, skills, and experience. Additional cash or stock incentives may be provided as part of the compensation package, in addition to a range of medical, financial, and/or other benefits, dependent on the position offered.
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.
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