ABOUT CAPELLA SPACE

Capella Space is a pioneer in Synthetic Aperture Radar (SAR) satellite technology and space-based signal intelligence. We empower government, commercial, and research organizations around the world with high-resolution, timely Earth insights that drive better decisions - across defense and intelligence, disaster response, infrastructure monitoring, environmental protection, energy, and more.
As a U.S.-based company, Capella designs, builds, and operates an advanced constellation of commercial SAR satellites in orbit today. Our satellites are backed by a fully automated tasking, collection, and delivery platform, delivering fast, reliable insights when and where our customers need them most. As a subsidiary of IonQ, a global leader in quantum computing, Capella is charting the future of Earth observation. Together, we’re on a mission to bring quantum computing to space - unlocking ultra-secure, encrypted uplink and downlink capabilities, reducing latency, and enabling powerful on-orbit analytics. This visionary integration of advanced remote sensing and quantum technology will support customers with the highest level of data fidelity, security, and speed.
Capella was named one of Fast Company’s 10 Most Innovative Companies in Space in 2023 and is a trusted partner of the U.S. Space Force, U.S. Air Force, U.S. Navy, NASA, the National Reconnaissance Office, and allied government agencies worldwide. Built In recognized IonQ on three of its 2025 Best Places to Work lists.

EXPERIENCE & SKILLS:

• 3+ years of hands-on experience in a Data Engineering role.
• A desire to build scalable, robust, and interesting platforms.
• Enjoys learning new technologies and approaches to problem-solving.
• Strong analytical and problem-solving skills, with a focus on building efficient systems.
• Detail-oriented with a passion for data quality, accuracy, and reliability.
• Excellent communication and cross-team collaboration skills.

CORE TECHNICAL SKILLS REQUIRED:

• Strong proficiency in Python for data engineering tasks (e.g., scripting, data manipulation, API integration).
• Strong proficiency in SQL for complex querying and data modeling.
• Proven experience building and maintaining data pipelines, especially from sources like third-party APIs and databases using Change Data Capture (CDC).
• Hands-on experience with a cloud data warehouse (Snowflake strongly preferred).
• Experience with data transformation tools, preferably DBT.
• Experience with CI/CD tools (e.g., GitLab CI).

ABOUT THE ROLE

Join our maturing DataOps team as a Data Analytics Engineer, where you will be at the heart of building our next-generation data analytics infrastructure. In this role, you will design, build, and optimize the scalable and reliable data pipelines that help our entire organization. You will be responsible for ingesting data from a variety of sources using modern techniques and tools. Your primary mission is to provide a clean, reliable, and timely data foundation within our data warehouse for your fellow engineers and business stakeholders to build upon. If you are passionate about building robust and scalable data systems from the ground up and want to make a significant impact on a data-driven team, this is the role for you.

ROLE RESPONSIBILITIES

• Data Pipeline Development & Ingestion: Design, build, and maintain efficient and resilient data pipelines to extract and load data into data warehouse from third-party APIs, databases (leveraging CDC), and various internal systems.
• Data Modeling & Transformation: Collaborate with teammates to implement and support dbt data models, ensuring raw data is effectively transformed for analytical use.
• Pipeline Orchestration & Monitoring: Assist in the orchestration, scheduling, and monitoring of data pipelines to ensure high availability, reliability, and timeliness of data.
• Data Quality & Testing: Implement data quality checks, validation rules, and test frameworks within the pipelines to ensure the accuracy and integrity of the data.
• Collaboration & Support: Act as the primary data provider for the DataOps team, ensuring clean, structured data needed for advanced modeling, analytics, and data application development.
• Documentation: Create and maintain comprehensive documentation for data pipelines, sources, and processes to support transparency and knowledge sharing.