The job, in the employer's words
Astranis builds advanced satellites for high orbits, expanding humanity’s reach into the solar system. Today Astranis satellites provide dedicated, secure networks to highly-sophisticated customers across the globe – large enterprises, sovereign governments, and the US military.
With five satellites on orbit and many more set to launch soon, the company is servicing a backlog of more than $1 billion of commercial contracts. Astranis is the preferred satellite communications partner for buyers with stringent requirements for uptime, data security, network visibility, and customization.
Astranis has raised over $1.2 billion from some of the world’s best investors, including Andreessen Horowitz, Baillie Gifford, Blackrock, Fidelity, Franklin Templeton, and Snowpoint, and employs a team of 500 engineers and entrepreneurs. Astranis designs, builds, and operates its satellites out of its 153,000 sq. ft. headquarters in Northern California, USA.
Senior Data Platform Engineer - Enterprise Systems
Astranis is scaling satellite production at a record pace. Building constellations of commercial geostationary spacecraft generates massive amounts of telemetry, manufacturing, supply chain, and application data. To keep our engineers moving fast, we need a unified, scalable data platform and observability infrastructure that brings clarity to every operational detail from the factory floor to launch.
We’re looking for a Senior Data Platform Engineer to define, architect, and build our core data architecture, unified logging pipelines, and internal data tools. You will conduct hands-on customer discovery across hardware, software, and operational teams to turn complex requirements into production-ready software tools and establish clear architectural patterns across our data stack.
If you're a platform engineer who thrives on customer discovery, systems architecture, and turning raw data into intelligence to scale our fleet, come build with us.
Role
What You’ll Build:
- Architectural Strategy & Tooling: Define our overarching data architecture, ingestion, storage, and access patterns separating transactional databases (Managed Postgres) from centralized analytical warehouses (BigQuery, Snowflake).
- Internal Customer Discovery & Project Design: Conduct discovery across the company, working with aerospace engineers, technicians, finance, sales, and software teams to gather requirements and build robust backend data tools and platforms.
- Enterprise Analytics Engine: Build standardized data pipelines and modeling frameworks (dbt) that stitch together telemetry, supply chain, factory floor (ION), and ERP (NetSuite) data into unified analytical stores for cross-functional reporting.
- Unified Logging & Observability Platform: Build end-to-end pipelines to collect, normalize, and query structured logs, traces, and hardware telemetry across backend services and factory testbeds for real-time system monitoring and debugging.
- Governance & Access Control: Establish corporate data governance frameworks, Role-Based Access Control (RBAC), and robust schema evolution strategies to guarantee data security, quality, and backward compatibility.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent technical experience.
- 5+ years of professional software/data engineering experience with a focus on data infrastructure, data platforms, or backend engineering.
- Strong proficiency with Python and solid software design fundamentals.
- Proven experience implementing RBAC, Data Governance, and Schema Evolution/Migration strategies in production environments.
- Deep database expertise in transactional, relational databases (PostgreSQL schema design, OLTP workflows) as well as columnar databases and analytical data warehouses (BigQuery, Snowflake).
- Experience with modern data orchestration and modeling tools (e.g., dbt, Airflow, Prefect, Dagster).
Bonus
- Domain Tools & Integrations: Prior experience building data integrations around hardware, PLM, MES, or ERP platforms (Altium, Arena, First Resonance ION, NetSuite).
- Aerospace & Manufacturing: Background working in hardware-centric environments such as aerospace, automotive, robotics, or complex precision manufacturing.
- APIs & Infrastructure: Experience designing, building, and maintaining robust APIs (RESTful, gRPC) and cloud infrastructure (Docker, Kubernetes/GKE, GCP).
Base pay is just one component of Astranis’s total rewards package. Your compensation also includes a significant equity package via incentive stock options, high-quality company-subsidized healthcare, disability and life insurance, 401(k) retirement planning, flexible PTO, and free on-site catered meals.
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Benefits mentioned
- All our positions offer a compensation package that includes equity and robust benefits.
- Base pay is just one component of Astranis’s total rewards package. Your compensation also includes a significant equity package via incentive stock options, high-quality company-subsidized healthcare, disability and life insurance, 401(k) retirement planning, flexible PTO, and free on-site catered meals.
- Astranis pay ranges are informed and defined through professional-grade salary surveys and compensation data sources. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, and the level and scope of the position.
- Base Salary
- $145,000—$210,000 USD
Requirements mentioned
- Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent technical experience.
- 5+ years of professional software/data engineering experience with a focus on data infrastructure, data platforms, or backend engineering.
- Strong proficiency with Python and solid software design fundamentals.
- Proven experience implementing RBAC, Data Governance, and Schema Evolution/Migration strategies in production environments.
- Deep database expertise in transactional, relational databases (PostgreSQL schema design, OLTP workflows) as well as columnar databases and analytical data warehouses (BigQuery, Snowflake).
- Experience with modern data orchestration and modeling tools (e.g., dbt, Airflow, Prefect, Dagster).
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