AWS Data Engineer

Technopride Ltd; City Of London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading data solutions firm is seeking an experienced Senior Data Engineer to develop next-generation data pipelines. In this role, you will design scalable workflows using Python and AWS services, apply software engineering best practices, and collaborate with stakeholders to create data-driven solutions. The ideal candidate will have over 10 years of experience and a solid understanding of data engineering fundamentals. This is an exciting opportunity to be part of a fast-paced, innovative environment.

Qualifications

• 10+ years of hands-on engineering experience.
• Strong background in building modern data pipelines.
• Experience with cloud-native technologies.
• Familiarity with data quality frameworks.

Responsibilities

• Design and develop scalable, testable data pipelines.
• Orchestrate data workflows using AWS services.
• Participate in code reviews and architectural design.

Skills

Python coding

Data pipeline development

AWS services (Glue, Lambda, EMR)

Apache Spark

Agile environments

Tools

Apache Spark

AWS S3

GitLab CI

Jenkins

Job description

Role Overview

We are developing a next-generation data platform and are looking for an experienced Senior Data Engineer to help shape its architecture, reliability, and scalability. The ideal candidate will have more than 10 years of hands‑on engineering experience and a strong background in building modern data pipelines, working with cloud-native technologies, and applying robust software engineering practices.

Design & Build Data Pipelines

• Design, develop, and optimise scalable, testable data pipelines using Python and Apache Spark.
• Implement batch workflows and ETL processes adhering to modern engineering standards.

Develop Cloud-Based Workflows

• Orchestrate data workflows using AWS services such as Glue, EMR Serverless, Lambda, and S3.
• Contribute to the evolution of lakehouse architecture leveraging Apache Iceberg.

Apply Software Engineering Best Practices

• Use version control, CI/CD pipelines, automated testing, and modular code principles.
• Participate in pair programming, code reviews, and architectural design sessions.

Data Quality & Observability

• Build monitoring and observability into data flows.
• Implement basic data quality checks and contribute to continuous improvements.

Stakeholder Collaboration

• Work closely with business teams to translate requirements into data-driven solutions.
• Develop an understanding of financial indices and share domain insights with the team.

What You’ll Bring

Technical Expertise

• Strong experience writing clean, maintainable Python code, ideally using type hints, linters, and test frameworks such as pytest.
• Solid understanding of data engineering fundamentals including batch processing, schema evolution, and ETL pipeline development.
• Experience with—or strong interest in learning—Apache Spark for large-scale data processing.
• Familiarity with AWS data ecosystem tools such as S3, Glue, Lambda, and EMR.

Ways of Working

• Comfortable working in Agile environments and contributing to collaborative team processes.
• Ability to engage with business stakeholders and understand the broader context behind technical requirements.

Nice-to-Have Skills

• Experience with Apache Iceberg or similar table formats (e.g., Delta Lake, Hudi).
• Familiarity with CI/CD platforms such as GitLab CI, Jenkins, or GitHub Actions.
• Exposure to data quality frameworks such as Great Expectations or Deequ.
• Interest or background in financial markets, index data, or investment analytics.