Data Engineer(Python/AWS/SQL)

Round 1: Technical coding interview (Python, data structures, algorithms) → Round 2: System design & architecture interview (data pipelines, AWS services) → Round 3: HR interview

LSEG promotes a collaborative and inclusive work culture with a strong emphasis on professional growth and work‑life balance. Employees enjoy flexible working arrangements, continuous learning opportunities, and a supportive environment that encourages innovation and teamwork.

Candidates typically go through a structured interview process: a first round of coding on Python, followed by a system design interview that tests knowledge of AWS services and data pipelines, and a final HR round. The difficulty is moderate to high, with emphasis on clean code, design patterns, and real‑world problem solving. Preparation tips include practicing coding problems, reviewing AWS architecture concepts, and building sample data pipelines.

Bachelor’s degree in Computer Science, Information Technology, Engineering or related field. Minimum 60% (or equivalent CGPA 6.5/10). No backlogs allowed. Candidates should have at least 1–2 years of relevant experience in software development or data engineering.

London Stock Exchange Group (LSEG) is a leading global financial markets infrastructure and data provider that powers indices trusted by financial institutions worldwide. With a presence in 65 countries and a workforce of over 25,000 employees, LSEG is at the forefront of delivering market data, analytics, and technology solutions that enable investors and regulators to make informed decisions. The company’s mission is to drive financial stability, empower economies, and create sustainable growth, while fostering a culture of integrity, partnership, excellence, and change. The Data Engineer (Python/AWS/SQL) role is a full‑time position based in Bengaluru’s Divyasree Technopolis campus. As part of the Future Talent pipeline, you will help build the next‑generation index data platform that processes large volumes of financial data in a highly available AWS environment. Your work will span from problem definition to production support, ensuring that the services you build are modular, well‑tested, and maintainable. Key Responsibilities: 1. Design, develop, and maintain Python services, APIs, and command‑line tools that interact with Aurora PostgreSQL and object storage. 2. Build data pipelines that process data at scale using AWS Lambda, S3, Glue, EMR Serverless, and containerised services. 3. Create and evolve data models, ensuring schema design, evolution, and backward compatibility. 4. Implement CI/CD pipelines, code reviews, and automated testing (unit, integration, contract) using Git‑based workflows. 5. Embed observability from the start: structured logging, metrics, tracing, and alerting. 6. Collaborate with product, business, and engineering teams to translate requirements into working software. 7. Participate in design and architecture discussions, contributing to shared engineering standards. 8. Mentor junior engineers and share knowledge through pair programming and code reviews. 9. Stay close to the codebase, debugging issues and ensuring high reliability. 10. Continuously learn and adopt new technologies relevant to financial indices and data engineering. Tech Stack: Python (type hints, mypy, black, pytest), AWS (Lambda, S3, Glue, EMR Serverless, IAM, IAM policies), Aurora PostgreSQL, Docker, Kubernetes, Git, CI/CD (GitHub Actions, Jenkins), structured logging, metrics, tracing. Growth Path: Engineers can progress from Junior Data Engineer to Senior Data Engineer, then to Lead or Principal roles, with opportunities to move into architecture, data platform, or product leadership positions. Why Join LSEG? The company offers competitive compensation, a collaborative culture that values ownership and continuous improvement, and exposure to cutting‑edge financial data services. Working at LSEG means contributing to products that impact global markets while enjoying a supportive environment that encourages learning and innovation.