Python Fullstack Developer

Associate Engineer — Data Engineering, API Integration & UI Development
In this role, you will: 
Work closely with Business stakeholders (Product Owner, SMEs, Ops and
control partners) to understand data and system goals, clarify requirements, and
translate them into actionable user stories with clear acceptance criteria.
Design and build data pipelines using Python and PySpark to ingest, transform,
and deliver data across structured and semi-structured sources, aligned to
agreed data architecture and quality standards.
Develop and integrate RESTful APIs to connect internal and third-party services,
ensuring reliable data exchange, robust error handling, and secure integration
patterns.
Build Angular-based UI components connected to backend APIs, delivering
intuitive, accessible, and performant frontend features.
Own delivery across the agile lifecycle: refinement, estimation, build, code
reviews, testing support, and release readiness.
Collaborate across PODs/teams to manage dependencies (APIs, shared
services, data contracts), and proactively identify risks, blockers and mitigation
options.
Ensure data and system quality through unit, integration, and API testing, with
strong practices in code review, version control, and peer collaboration.
Drive continuous improvement and knowledge sharing within the POD, helping
uplift engineering and delivery efficiency.
Requirements :
Hands-on experience with Python and PySpark for building data pipelines,
ETL/ELT processes, and working with large-scale structured and semi-structured
datasets.
Solid API integration capability: RESTful API design and consumption,
integration patterns, data transformation between systems, error handling, and
security considerations.
Proven Angular frontend development skills: component-based UI design, state
management, API-connected interfaces, and awareness of accessibility and UI
performance fundamentals.

INTERNA
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Proven ability to work directly with Business stakeholders—comfortable
running/participating in requirement workshops, asking the right questions, and
converting ambiguity into actionable deliverables.
Familiarity with data engineering concepts: data contracts/schemas, data quality,
pipeline orchestration, and working knowledge of SQL and cloud data platforms.
Experience with version control (Git), automated testing approaches (unit,
integration, API/UI testing), and agile/SAFe delivery practices.
Good awareness of observability basics (logging/monitoring) and a pragmatic
mindset for diagnosing data and integration issues.
Understanding of CI/CD practices and deployment pipelines sufficient to
contribute to reliable, repeatable releases.