Staff Software Engineer
Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Oct 27, 2026.
Digantara is a leading Space Surveillance and Intelligence company focused on ensuring orbital safety and sustainability. With expertise in space-based detection, tracking, identification, and monitoring, Digantara provides comprehensive domain awareness across all regimes, enabling end-users to gain actionable intelligence on a single platform. At the core of its infrastructure lies a sophisticated integration of hardware and software capabilities aligned with the key principles of situational awareness: perception (data collection), comprehension (data processing), and prediction (analytics). This holistic approach empowers Digantara to monitor all Resident Space Objects (RSOs) in orbit, fostering comprehensive domain awareness.
Why Us?
Be part of a collaborative and innovative environment where your ideas and skills make a real difference to the entire space realm.
Push the boundaries with hands-on experience, greater responsibilities, and rapid career advancement.
Competitive incentives, galvanising workspace, blazing team-pretty much everything you have heard about a startup.
Ideal Candidate:
We are looking for a Staff Software Engineer to help build and evolve the platforms and product capabilities supporting mission software and data-intensive workflows at Digantara.
You will bring strong backend and data engineering expertise, along with a practical understanding of frontend architecture. Working closely with the Platform Lead and engineering teams, you will lead complex technical initiatives, build shared capabilities, and improve the reliability and performance of our systems.
This is a hands-on individual contributor role with technical influence across teams.
What you will do?
Lead technical initiatives from problem definition and architecture through implementation, rollout, and operational support.
Design and build backend services, APIs, shared components, and internal tools that support product and mission requirements.
Build maintainable data pipelines in Python, using Apache Spark or comparable frameworks where distributed processing is appropriate.
Develop batch and streaming workflows to ingest, validate, transform, and serve data from multiple sources.
Improve pipeline performance through partitioning, parallel processing, incremental computation, and efficient data access.
Establish reliable processing patterns, including retries, idempotency, backfills, data quality checks, and monitoring.
Evaluate orchestration and streaming tools such as Apache Airflow, Dagster, Kafka, or equivalent technologies, based on workload needs.
Work with frontend engineers on API contracts, authentication, authorization, real-time data delivery, and application performance.
Improve deployment tooling, CI/CD, observability, security, and developer experience.
Partner with algorithm engineers to integrate scientific workloads into production systems, preserving their ownership of scientific correctness and algorithm validation.
Mentor engineers through design discussions, code reviews, pairing, and technical documentation.
Success in this role includes strengthening technical ownership throughout the team and helping engineers make sound decisions independently.
What we are looking for:
Experience building and operating production software, with demonstrated technical impact across multiple services or teams.
Strong Python / Go engineering skills, including testing, profiling, packaging, and building maintainable services and pipelines.
Strong backend fundamentals across APIs, databases, asynchronous processing, and distributed systems.
Hands-on experience with Apache Spark or a comparable distributed processing framework, including performance tuning and troubleshooting.
Understanding of distributed processing concepts such as MapReduce, partitioning, shuffles, joins, and aggregations, and when distributed execution is appropriate.
Experience with workflow orchestration, dependency management, incremental processing, and pipeline recovery.
A practical understanding of modern frontend engineering, including JavaScript or TypeScript, a framework such as React, browser behavior, state management, and frontend-backend integration.
Experience with cloud infrastructure, containers, CI/CD, infrastructure as code, and production observability.
Ability to balance reliability, latency, throughput, cost, and implementation complexity.
Clear technical communication and experience translating ambiguous requirements into practical designs and working software.
A collaborative approach to technical leadership, with experience mentoring engineers and partnering with engineering leads.
Useful additional experience:
Scientific, geospatial, time-series, or sensor data processing.
Satellite, aerospace, or mission-critical software.
Data lake and lakehouse technologies such as Parquet, Apache Iceberg, or Delta Lake.
Integrating computational algorithms into production services.
Building shared platforms used by multiple product teams.
Operating software in on-premises or restricted deployment environments.
What success looks like:
Product teams can adopt shared platform capabilities with less integration effort.
Data pipelines and services become more reliable, observable, and efficient.
Frontend and backend teams work with clear, stable integration contracts.
Deployment and development workflows become simpler and more predictable.
Engineers build greater ownership of technical decisions and the systems they maintain.
Platform investments support measurable product and mission outcomes.