Data Engineer - Customer Intelligence Platform

Location: Warsaw
Contract Type: Contract

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  Build scalable data pipelines across AWS, PySpark and Snowflake that directly power customer-facing decisioning

  
  


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  Work with production-grade ML systems operationalising segmentation, propensity and value models at scale

  
  


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  Join a cross-functional engineering team modernising customer intelligence capabilities across digital and marketing channels

  
  


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  Develop robust, automated solutions reducing manual intervention and improving data quality across critical use cases

  
  


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  Contribute to infrastructure-as-code and DevOps practices using GitHub Actions and Terraform

  
  

Company Overview

Our client is a leading financial services organisation based in Warsaw, specialising in customer intelligence and decisioning platforms. They build sophisticated systems that enable personalised pricing, targeted offers and campaign execution across digital, CRM and marketing channels. The organisation is committed to engineering excellence, data quality and maintaining the highest standards of information security and governance. They're investing in modernising their data infrastructure and expanding their engineering capabilities to support continued growth and innovation.

Our client is seeking an experienced Data Engineer to join their customer intelligence team. If you have strong expertise in AWS data engineering, PySpark and Snowflake, and you're looking to advance your career building production-grade data systems that impact millions of customer interactions, this role offers a genuine opportunity to make a significant contribution.

Position Overview

As a Data Engineer, you'll design and maintain production-grade data pipelines that operationalise customer models into resilient decisioning flows. You'll work across the full data stack - from ingestion through transformation to deployment - ensuring data quality, pipeline robustness and operational reliability. Your work will directly support critical customer-facing use cases including personalised pricing, recommendations and campaign execution. You'll collaborate with product, data science and engineering teams to evolve platforms in line with the organisation's modernisation programme, whilst ensuring all solutions meet rigorous information security and governance standards.

Responsibilities

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  Design, build and maintain production-grade data pipelines and services across AWS, PySpark, Snowflake and SQL transformation frameworks

  
  


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  Operationalise customer-level models such as segmentation, propensity and value models into scalable decisioning flows for downstream consumption

  
  


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  Develop modular, testable and high-quality data models and transformation pipelines using DBT or equivalent structured SQL frameworks

  
  


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  Improve pipeline robustness through automation, monitoring, validation and dependency management to reduce manual intervention and production failures

  
  


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  Diagnose and resolve pipeline failures, support reruns and validations, and address data quality issues maintaining continuity of critical use cases

  
  


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  Contribute to CICD and DevOps practices including GitHub Actions, version control, testing disciplines and Infrastructure-as-Code approaches such as Terraform

  
  


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  Collaborate with product, data science and engineering teams to operationalise new use cases and evolve platforms

  
  


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  Ensure all solutions align with information security, data protection, governance standards and auditability requirements for customer data and model processing

  
  

Requirements

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  6-8 years of hands-on experience with data engineering, cloud platforms or related roles

  
  


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  Strong experience with AWS data stack including S3, EMR, EC2 and related cloud-native data engineering services

  
  


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  Advanced PySpark expertise including development of production-grade pipelines, performance optimisation and large-scale data processing

  
  


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  Strong Snowflake experience including data modelling, preferably using RDV, BDV or similar patterns, and query performance tuning

  
  


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  Proven experience using DBT or equivalent SQL transformation frameworks to develop modular, testable and well-structured data models

  
  


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  Hands-on experience with CICD and DevOps practices including GitHub Actions or similar tooling

  
  


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  Exposure to Infrastructure-as-Code approaches such as Terraform

  
  


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  Experience with machine learning pipelines and operationalising ML models in production environments

  
  


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  Strong understanding of data quality, testing disciplines and engineering best practices

  
  


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  Knowledge of information security, data protection and governance standards

  
  

Benefits

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  Competitive salary package commensurate with experience

  
  


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  Opportunity to work with cutting-edge data technologies and cloud infrastructure

  
  


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  Professional development and training in emerging data engineering practices

  
  


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  Exposure to machine learning and advanced analytics at scale

  
  


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  Collaborative environment with experienced data science and engineering teams

  
  

Alongside a competitive benefits package, you'll join a forward-thinking organisation that values technical excellence, continuous learning and collaborative problem-solving. You'll work with talented engineers and data scientists, contributing to systems that directly impact customer experience and business outcomes.

How to Apply

To apply for this role, please submit your CV using the form below or email [email protected]