Data Engineering Manager, Allwyn UK, GB

The Data Engineering Manager reports directly to the BI Delivery Manager and is responsible for leading a

team of data engineers in designing, building, and maintaining highly scalable, robust, and reliable data

pipelines within an AWS environment. This role drives the strategic direction for data warehousing, data

modeling, data processing frameworks, and big data initiatives—ensuring data is captured, processed, and

delivered seamlessly to support business intelligence, reporting, and advanced analytics.

A strong candidate will have deep hands-on experience with AWS services (particularly Redshift, Glue, S3), be

adept at managing inbound and outbound integrations with external systems, and excel at mentoring team

members. The Data Engineering Manager will also collaborate extensively with cross-functional

stakeholders—such as product, analytics, reporting, and business teams—to align engineering efforts with

organizational data goals. Experience with Airflow (or a similar workflow orchestration tool) is critical for effective data job scheduling and performance optimization.

We are Allwyn UK, part of the Allwyn Entertainment Group – a multi-national lottery operator with a

market-leading presence in Austria, the Czech Republic, Greece, Cyprus and Italy. We have been officially

awarded the Fourth Licence (10 year licence) to operate the National Lottery starting February 2024.

We’ve developed ground-breaking technologies, built player protection frameworks, and have a proven

track record of making lotteries better. Our aim is to create one of the UK’s most inclusive organisations –

where people can bring the best of themselves, to do their best work, every day, for the benefit of good

causes.

Allwyn is an Equal Opportunity Employer which prides itself in being diverse and inclusive. We do not

tolerate discrimination, harassment, or victimisation in the workplace. All employment decisions at Allwyn

are based on the business needs, the job requirements, and the individual qualifications. Allwyn

encourages applications from individuals regardless of age, disability (visible or hidden), sex, gender

reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil

partnerships.

While the main contribution of the National Lottery to society is through the funds to good causes, at

Allwyn we put our purpose and values at the heart of everything we do. Join us as we embark on a once-in-

a-lifetime, largescale transformation journey to build a bigger, better, and safer National Lottery that

delivers more money to good causes.

Our goal is to create one of the uk’s most inclusive organisations – where people can bring the best of themselves,

To do their best work, every day, for the benefit of good causes.

Allwyn is an Equal Opportunity Employer which prides itself in being diverse and inclusive. We do not tolerate

discrimination, harassment, or victimisation in the workplace. All employment decisions at Allwyn are based

on the business needs, the job requirements, and the individual qualifications. Allwyn encourages

applications from individuals regardless of age, disability (visible or hidden), sex, gender reassignment, sexual

orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships.

Responsibilities

1. Team Leadership & Management

 · Recruit, mentor, and lead a team of data engineers, providing both technical and professional guidance.

· Foster a culture of ownership, continuous learning, and collaboration within the team.

2. ETL/ELT Development & Architecture

· Design, develop, and optimize ETL/ELT pipelines using AWS Glue, Redshift, and related AWS services.

· Establish best practices for data ingestion, transformations, orchestration, and scheduling.

· Oversee the development of inbound (external-to-internal) and outbound (internal-to-external) data feeds to ensure seamless integration with third-party services and downstream applications.

3. Data Warehouse & Data Modeling

· Create and maintain scalable data models in Redshift to support business intelligence and reporting needs.

· Ensure data architecture adheres to industry best practices, including normalization/denormalization strategies and performance optimization.

· Develop and maintain big data solutions (e.g., Spark, EMR, Hadoop) for large-scale data processing where needed.

4. Infrastructure & Automation

· Implement and manage data lake solutions with AWS S3 and associated ingestion strategies.

· Leverage Infrastructure-as-Code (e.g., CloudFormation, Terraform) to automate resource provisioning and configuration.

5. Effort Estimation & Resource Planning

· Analyze project requirements to accurately estimate the level of effort and resources needed.

· Develop realistic timelines and project plans, ensuring the team delivers solutions on schedule and within budget.

· Monitor ongoing projects against estimates, adjusting plans and resources as needed.

  6. Collaboration, Engagement & Design Alignment

· Partner with analytics, product, business, and reporting teams to gather data requirements and translate them into technical solutions.

· Conduct design reviews and regularly synchronize with the reporting team to ensure data pipelines and models align with reporting dashboards and analytics needs.

 · Engage with vendor solutions and external technical teams when necessary to enhance or troubleshoot data pipelines.

7. Governance & Compliance

· Implement data governance best practices, ensuring data quality, integrity, and security across pipelines.

· Collaborate with Security and Compliance teams to adhere to data privacy regulations and internal policies.

8. Performance Monitoring & Optimization

· Establish and monitor metrics for ETL performance, proactively addressing bottlenecks and performance issues.

· Continuously review and optimize Redshift cluster configurations, workload management (WLM), and query performance.

· Oversee and optimize big data workloads and streaming pipelines to manage large-scale, high-velocity data ingestion.

9. Release Planning & Deployment of the Data Platform

· Oversee the end-to-end release cycle for data platform enhancements, including scheduling, coordination, and communication with cross-functional teams.

· Ensure thorough testing, documentation, and rollback strategies are in place prior to deployment.

10. ETL Jobs Orchestration & Performance Optimization

· Manage data job orchestration using Airflow (or similar tools, e.g., Step Functions) for efficient scheduling, coordination, and monitoring of data pipelines.

· Continuously analyze and optimize ETL job performance, identifying opportunities for parallelization, resource tuning, and cost efficiency.

· Ensure Airflow DAGs are designed using best practices for error handling, retry strategies, and alerting.

11. Support for Data Ops & Priority Issue Resolution

· Collaborate closely with the Data Ops team to swiftly resolve high-priority issues, ensuring minimal impact on business operations.

· Serve as escalation point for complex data engineering challenges, providing expert guidance in troubleshooting pipeline failures.

12. Code Review & Efficient Unit Testing Practices

· Implement and enforce best practices for code reviews, ensuring adherence to quality and security standards.

· Promote efficient unit testing frameworks and methodologies to reduce defects, enhance reliability, and accelerate development cycles.