Data Engineer

Airswift; Birmingham; Hybrid; GBP 101,000 - 166,000; Full time

Job summary

Airswift is looking for a Senior Data Engineer in Birmingham to design, build and optimise enterprise-scale data platforms that support AI, ML and advanced analytics. The role is a contract, full-time workload with a hybrid setup (3 days onsite).

You will develop high-throughput data pipelines using Spark and AWS services, implement lakehouse architectures, and ensure governance, security and observability across large datasets.

Qualifications

• Minimum 5 years in data engineering or equivalent.
• Strong Spark development in Java/Scala.
• Hands-on AWS experience (S3, EMR, Glue, IAM).
• Advanced SQL for complex transformations.
• Experience building ETL/ELT pipelines & lakehouse solutions.
• Exposure to AI/ML data ecosystems.
• Solid data governance, security & resilience practices.

Responsibilities

• Design, build, and maintain high-throughput data pipelines with Spark.
• Develop scalable ETL/ELT solutions for structured/unstructured data.
• Support AI/ML/Generative AI initiatives with data platforms.
• Implement lakehouse architectures for scalability and analytics.
• Optimize SQL transformations across large datasets.
• Enforce data governance, security, monitoring and reliability.
• Collaborate with cross-functional teams to boost automation and observability.

Skills

5+ years of Data Engineering

Spark Java/Scala

AWS (S3, EMR, Glue, IAM)

Advanced SQL

ETL/ELT pipelines

AI/ML data ecosystems

Data governance & security

Snowflake

Observability & DevOps

Tools

Snowflake

Observability tools

DevOps practices

Job description

Duration: Contract

Workload: Full time hours

Setup: Freelance (Daily Rate / Limited Company / Umbrella / Sole Trader)

Location: Birmingham (Hybrid, 3 days onsite)

Our client is seeking a Senior Data Engineer to design and optimise enterprise-scale data platforms that support AI, Machine Learning, and advanced analytics initiatives.

Key Responsibilities:

• Design, build, and maintain high-throughput data pipelines using Apache Spark and cloud-native AWS technologies.


• Develop scalable ETL/ELT solutions for structured and unstructured data.


• Support AI/ML and Generative AI initiatives by enabling robust, data-driven platforms.


• Implement modern lakehouse architectures to improve scalability, resilience, and analytics capabilities.


• Optimise SQL-based data transformations and processing performance across large datasets.


• Ensure best practices around data governance, security, monitoring, and reliability.


• Collaborate with cross-functional engineering teams to improve automation, observability, and platform performance.

Must-Have Skills:

• 5+ years of Data Engineering experience.


• Strong Apache Spark development skills using Java or Scala.


• Hands-on experience with AWS services such as S3, EMR, Glue, and IAM.


• Advanced SQL expertise.


• Experience building ETL/ELT pipelines and data lakehouse solutions.


• Exposure to AI/ML or Generative AI data ecosystems.


• Strong understanding of data governance, security, and operational resilience.

Nice-to-Have:

• Snowflake experience.


• Knowledge of observability tooling, automation, and DevOps practices