Your space-enabled career begins here

As part of the ML Data Engineering team, you will be contributing to advancing and managing our satellite imagery data pipeline and infrastructure. You will be collaborating closely with cross-functional teams including the machine learning team, satellite team, and data science specialists. Beyond technical expertise, your role will involve shaping data products, continuous learning, and upholding best practices. Your experience and contributions will be instrumental in driving process improvements and fostering collaboration across the organization.

Data Acquisition & Management:

Download satellite imagery from various sources and APIs

Curate and organize satellite imagery datasets according to established standards

Build and maintain comprehensive data catalogs with proper metadata tagging

Ensure data quality and integrity throughout the acquisition process

Infrastructure & Storage:

Optimize storage costs while maintaining data accessibility and performance

Monitor and maintain data backup and recovery systems

Data Labeling & Annotation:

Coordinate and execute satellite imagery labeling initiatives with data scientists

Develop and implement quality control processes for labeled datasets

Work with domain experts to establish labeling guidelines and standards

Manage labeling workflows and delivery to ML teams

MLOps & Pipeline Support:

Support the development and maintenance of automated data pipelines

Implement data versioning and lineage tracking

Collaborate on model training data preparation and validation

Assist in deploying and monitoring data processing workflows

Cross-functional Collaboration:

Work closely with the satellite team to understand data requirements

Support the machine learning team with timely data delivery

Participate in technical discussions and provide data-driven insights

Document processes and maintain technical documentation

Who Should Apply

We welcome final-year students, recent graduates, and early-career professionals (0–1 year experience) with a passion for data systems and applied ML. We encourage applicants from any academic background with the right technical skills and willingness to learn.

Technical Skills:

Strong programming knowledge in Python

Strong problem-solving skills and debugging ability

Strong programming skills in Python and familiarity with data processing libraries (pandas, numpy, etc.)

Experience with cloud platforms (AWS, Google Cloud, or Azure) and storage solutions

Understanding of database systems (both SQL and NoSQL)

Familiarity with data pipeline tools and workflow orchestration

Basic knowledge of containerization (Docker) and version control (Git)

(Bonus) Understanding of satellite imagery and geospatial formats

Personal Attributes:

Rapid in response, flexible to changes, and nimble in approach without compromising on the overall quality of work.

Develops solutions through an adequate mix of intuition, reason and logic

Pushes the collective quality of thought to new limits while trusting and respecting others' skillsets & intentions

Communicates with utmost clarity, while maintaining the highest standards of candor

Adopts simplicity as a clutter breaking mechanism to waste fewer resources and time

Strives for perfection, iteratively, to deliver work that is above and beyond accepted standards of excellence

Work Experience

Entry-level position suitable for recent graduates or candidates with 0-1 years of relevant experience in data engineering, data science, or related fields

Learning & Growth:

Hands-on experience with cutting-edge satellite imagery and geospatial technologies

Exposure to enterprise-scale data infrastructure and MLOps practices

Mentorship from experienced data engineers and ML practitioners

Opportunity to work on real-world problems with global impact

Technical Exposure:

Work with petabyte-scale satellite imagery datasets

Learn industry best practices for data governance and quality management

Gain experience with modern data stack and infrastructure-as-code

Understand the complete ML lifecycle from data to deployment

Career Development:

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Clear path for conversion to full-time role based on performance

Opportunity to shape and influence data strategy and processes

Exposure to cross-functional collaboration in a fast-paced environment

Access to continuous learning resources and professional development