

Randstad Digital
Data & AI - LLM Model Developer(PySpark Engineer)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a 5-month contract for a Lead PySpark Engineer focused on migrating legacy data workflows to AWS. Requires 5+ years of PySpark experience, strong AWS knowledge, and financial services background. Remote work based in the UK.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
February 10, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Fixed Term
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🔒 - Security
Unknown
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📍 - Location detailed
United Kingdom
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🧠 - Skills detailed
#AI (Artificial Intelligence) #SQL (Structured Query Language) #Spark SQL #Python #Documentation #AWS (Amazon Web Services) #Data Accuracy #"ETL (Extract #Transform #Load)" #Quality Assurance #Migration #Base #Macros #S3 (Amazon Simple Storage Service) #GIT #Cloud #Debugging #Athena #Data Modeling #Spark (Apache Spark) #Data Mart #SAS #Scala #PySpark
Role description
Lead PySpark Engineer (Cloud Migration)
Role Type: 5-Month Contract
Location: Remote (UK-Based)
Experience Level: Lead / Senior (5+ years PySpark)
Role Overview
We are seeking a Lead PySpark Engineer to drive a large-scale data modernisation project, transitioning legacy data workflows into a high-performance AWS cloud environment. This is a hands-on technical role focused on converting legacy SAS code into production-ready PySpark pipelines within a complex financial services landscape.
Key Responsibilities
• Code Conversion: Lead the end-to-end migration of SAS code (Base SAS, Macros, DI Studio) to PySpark using automated tools (SAS2PY) and manual refactoring.
• Pipeline Engineering: Design, build, and troubleshoot complex ETL/ELT workflows and data marts on AWS.
• Performance Tuning: Optimise Spark workloads for execution efficiency, partitioning, and cost-effectiveness.
• Quality Assurance: Implement clean coding principles, modular design, and robust unit/comparative testing to ensure data accuracy throughout the migration.
• Engineering Excellence: Maintain Git-based workflows, CI/CD integration, and comprehensive technical documentation.
Technical Requirements
• PySpark (P3): 5+ years of hands-on experience writing scalable, production-grade PySpark/Spark SQL.
• AWS Data Stack (P3): Strong proficiency in EMR, Glue, S3, Athena, and Glue Workflows.
• SAS Knowledge (P1): Solid foundation in SAS to enable the understanding and debugging of legacy logic for conversion.
• Data Modeling: Expertise in ETL/ELT, dimensions, facts, SCDs, and data mart architecture.
• Engineering Quality: Experience with parameterisation, exception handling, and modular Python design.
Additional Details
• Industry: Financial Services experience is highly desirable.
• Working Pattern: Fully remote with internal team collaboration days.
• Benefits: 33 days holiday entitlement (pro-rata).
Lead PySpark Engineer (Cloud Migration)
Role Type: 5-Month Contract
Location: Remote (UK-Based)
Experience Level: Lead / Senior (5+ years PySpark)
Role Overview
We are seeking a Lead PySpark Engineer to drive a large-scale data modernisation project, transitioning legacy data workflows into a high-performance AWS cloud environment. This is a hands-on technical role focused on converting legacy SAS code into production-ready PySpark pipelines within a complex financial services landscape.
Key Responsibilities
• Code Conversion: Lead the end-to-end migration of SAS code (Base SAS, Macros, DI Studio) to PySpark using automated tools (SAS2PY) and manual refactoring.
• Pipeline Engineering: Design, build, and troubleshoot complex ETL/ELT workflows and data marts on AWS.
• Performance Tuning: Optimise Spark workloads for execution efficiency, partitioning, and cost-effectiveness.
• Quality Assurance: Implement clean coding principles, modular design, and robust unit/comparative testing to ensure data accuracy throughout the migration.
• Engineering Excellence: Maintain Git-based workflows, CI/CD integration, and comprehensive technical documentation.
Technical Requirements
• PySpark (P3): 5+ years of hands-on experience writing scalable, production-grade PySpark/Spark SQL.
• AWS Data Stack (P3): Strong proficiency in EMR, Glue, S3, Athena, and Glue Workflows.
• SAS Knowledge (P1): Solid foundation in SAS to enable the understanding and debugging of legacy logic for conversion.
• Data Modeling: Expertise in ETL/ELT, dimensions, facts, SCDs, and data mart architecture.
• Engineering Quality: Experience with parameterisation, exception handling, and modular Python design.
Additional Details
• Industry: Financial Services experience is highly desirable.
• Working Pattern: Fully remote with internal team collaboration days.
• Benefits: 33 days holiday entitlement (pro-rata).






