AWS Data Engineer

FalconSmartIT; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

FalconSmartIT in London is seeking an AWS Data Engineer to design and build scalable data pipelines using Python and Apache Spark, with hybrid work in mind.

You will orchestrate data workflows using AWS Glue, EMR Serverless, Lambda and S3, integrate lakehouse architecture with Apache Iceberg, and collaborate with business teams.

The role emphasizes modern software practices, observability, testing with pytest, CI/CD, and active participation in code reviews and architecture discussions.

Qualifications

• Experience building scalable data pipelines in Python and Spark.
• Strong Python and Spark/ PySpark skills with a functional mindset.
• Familiarity with AWS data services and lakehouse architectures.
• Ability to translate business requirements into data-driven solutions.

Responsibilities

• Design scalable, testable data pipelines in Python and Spark.
• Orchestrate data workflows with AWS tools.
• Contribute to lakehouse architecture using Apache Iceberg.
• Collaborate with stakeholders in Agile teams and reviews.
• Build observability and basic quality checks into data flows.
• Participate in code reviews and architecture discussions.

Skills

Python

PySpark

AWS

Data pipelines

CI/CD

Testing (pytest)

Type hints

Agile

Observability

Spark

ETL

Tools

AWS Glue

EMR Serverless

Lambda

S3

GitLab CI

Jenkins

GitHub Actions

Apache Iceberg

Job description

Job Title: AWS Data Engineer

Job Location: London, UK/ Hybrid

Job Type: 12 Months FTC

Job Description:

Need Expert in Python Pyspark, AWS, Cloud, AWS Services, AWS Components

Designing and developing scalable, testable data pipelines using Python and Apache Spark

Orchestrating data workflows with AWS tools like Glue, EMR Serverless, Lambda, and S3

Applying modern software engineering practices: version control, CI/CD, modular design, and automated testing

Contributing to the development of a lakehouse architecture using Apache Iceberg

Collaborating with business teams to translate requirements into data-driven solutions

Building observability into data flows and implementing basic quality checks

Participating in code reviews, pair programming, and architecture discussions

Continuously learning about the financial indices domain and sharing insights with the team

WHAT YOU'LL BRING:

• Writes clean, maintainable Python code (ideally with type hints, linters, and tests like pytest)
• Understands data engineering basics: batch processing, schema evolution, and building ETL pipelines
• Has experience with or is eager to learn Apache Spark for large-scale data processing
• Is familiar with the AWS data stack (e.g. S3, Glue, Lambda, EMR)
• Enjoys learning the business context and working closely with stakeholders Works well in Agile teams and values collaboration over solo heroics

Nice-to-haves:

• It s great (but not required) if you also bring:
• Experience with Apache Iceberg or similar table formats
• Familiarity with CI/CD tools like GitLab CI, Jenkins, or GitHub Actions
• Exposure to data quality frameworks like Great Expectations or Deequ
• Curiosity about financial markets, index data, or investment analyticsjOBJ