Global Data Engineer

Position Overview

• Location Type: Remote
• Job Type: Full-time
• Salary: Not specified

epay is seeking a mid-level Data Engineer with at least 2 years of experience in Azure and Databricks. This role involves transforming complex datasets into actionable insights for internal stakeholders and external partners, focusing on pipeline development, categorization, and optimization for AI/ML use cases. You will work with diverse datasets across prepaid, financial services, gambling, and payments. Collaboration with analytics and product functions is essential, requiring flexibility to switch between roles. The position includes occasional global travel and requires flexibility across time zones for international team collaboration. While remote, frequent attendance at one of three UK locations (Billericay, Bracknell, or Baker Street, London) is required.

Key Benefits

• Be part of a high-performing team building modern, scalable data solutions used globally.
• Work hands-on with cutting-edge Azure technologies, with a strong focus on Databricks and Python development.
• Play a key role in evolving epay’s data architecture and ML-enablement strategies.

First Few Months' Focus

• Take ownership of a data pipeline or transformation flow within Databricks and contribute to its optimization and reliability.
• Work across raw and curated datasets to deliver categorized and enriched data ready for analytics and machine learning use cases.
• Provide support to analysts and financial stakeholders to validate and improve data accuracy.
• Collaborate with the wider team to scope, test, and deploy improvements to data quality and model inputs.
• Bring forward best practices from prior experience to help shape data cleaning, structuring, and processing.
• Demonstrate awareness of cost, latency, and scale when deploying cloud-based data services.

Ideal Candidate Profile

The ideal candidate understands they are part of a team and is willing to occupy various roles to allow the team to adjust work more effectively.

Responsibilities

• Data Pipeline Development: Build and maintain batch and streaming pipelines using Azure Data Factory and Azure Databricks.
• Data Categorization & Enrichment: Structure unprocessed datasets through tagging, standardization, and feature engineering.
• Automation & Scripting: Use Python to automate ingestion, transformation, and validation processes.
• ML Readiness: Work closely with data scientists to shape training datasets, applying sound feature selection techniques.
• Data Validation & Quality Assurance: Ensure accuracy and consistency across data pipelines with structured QA checks.
• Collaboration: Partner with analysts, product teams, and engineering stakeholders to deliver usable and trusted data products.
• Documentation & Stewardship: Document processes clearly and contribute to internal knowledge sharing and data governance.
• Platform Scaling: Monitor and tune infrastructure for cost-efficiency, performance, and reliability as data volumes grow.
• On-Call Support: Participate in an on-call rota system to provide support for the production environment, ensuring timely resolution of incidents and maintaining system stability outside of standard working hours.

Requirements

• Experience: 2+ years of professional experience in a data engineering or similar role.
• Skills: Proficiency in Azure and Databricks.
• Proactiveness: Willingness to develop and implement innovative solutions.