Sperry Rail
Full Stack Engineer- Data Science Focus
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Role Summary
We are looking for Full Stack Engineers with a data science focus to design, build, and maintain the data-driven applications and pipelines behind Zula 2.0, the platform that ranks and fuses the output of our rail inspection systems. You will work across the full stack with a strong emphasis on backend data processing, analytics, and machine learning workflows, all hosted on AWS cloud infrastructure. These seats sit close to the ranking and fusion logic itself, so the work is as much about understanding what the system should surface and in what order as it is about serving it. Zula 2.0 is the critical path for the team through the end of the year.
What We Expect From You
We expect an exceptional level of drive and ambition. You think beyond today's work to what the team and organisation need next, champion bold ideas, and see them through. Your hunger is infectious – it inspires those around you to aim higher. You should be someone who puts the team first. You share credit openly, admit when you are wrong, and welcome feedback as an opportunity to grow. You are comfortable saying "I don't know" and asking for help when needed. Strong interpersonal skills are important for this role. You should have good instincts for working with different people, listen actively, navigate disagreements constructively, and communicate clearly with both technical and non-technical audiences. This role requires a high degree of self-direction. You will manage complex work with minimal oversight, identify problems and solutions proactively, and may lead workstreams or small teams. You make well-reasoned technical decisions and only escalate when there is genuine business or architectural impact. Strong analytical thinking is critical. You will need to decompose ambiguous, multi-faceted problems, reason about complex distributed systems and data flows, and anticipate the downstream consequences of design decisions. You translate business problems into elegant technical solutions.
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Key Responsibilities
- Design and develop data pipelines, ETL processes, and backend services using Python
- Build and maintain APIs and microservices that serve data products to internal and external consumers
- Work with large-scale rail inspection datasets including ultrasonic, electromagnetic, and operational data
- Implement and deploy machine learning models and statistical analyses in production environments
- Architect and manage AWS cloud infrastructure for data workloads (S3, Lambda, Glue, Athena, SageMaker, Step Functions)
- Collaborate with domain experts to translate rail inspection requirements into technical solutions
- Write clean, tested, well-documented code following engineering best practices
- Participate in code reviews, sprint planning, and technical design discussions
- Collaborate with cross-functional teams including inspection engineers, data scientists, and product managers
- Contribute to a culture of continuous improvement, knowledge sharing, and technical excellence
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Required Skills & Qualifications
- Strong proficiency in Python (NumPy, Pandas, Scikit-learn, or similar)
- Experience building data pipelines and working with structured and unstructured data
- Solid understanding of SQL and relational and non-relational databases
- Experience with AWS cloud services (S3, Lambda, EC2, RDS, Glue, or similar)
- Strong applied mathematics – statistics, probability, linear algebra, or signal processing
- Demonstrated ability to read, understand, and extend complex algorithms and dense business logic written by others
- Understanding of software engineering principles (testing, code quality, design patterns)
- Familiarity with version control (Git), CI/CD pipelines, and agile development practices
- Strong problem-solving skills and ability to learn new technologies quickly
- Good communication skills – able to explain technical concepts to non-technical stakeholders
- A collaborative, team-first mindset aligned with our values of being Humble, Hungry, and Smart
Desirable Skills
- Exposure to time-series data, geospatial data, or IoT data streams
- Experience with ranking, scoring, or data fusion systems
- Familiarity with frontend technologies (React, TypeScript)
- Experience in rail testing, NDT, or sensor-based inspection industries (ultrasound, eddy current, electromagnetic, etc.)
- AWS certifications
- Experience with infrastructure-as-code (CloudFormation, CDK, Terraform)
- Knowledge of containerisation (Docker, ECS/Fargate)
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