Senior Data Engineer

The role 

As a Senior Data Engineer supporting the Risk and Operations Data Engineering team, you will design, build, and operate innovative data solutions within a fast-paced Agile environment. You'll play a key role in modernizing our data infrastructure and transforming our AWS‑centric data landscape, enabling advanced analytics, self‑service BI, and generative AI–driven insights. 

You'll work closely with stakeholders in Risk, Finance, Legal, Regulatory, and other operational areas, turning business requirements into robust data products and platforms that support reporting, analytics, and data‑driven decision-making. 

What you will do 

Data Platform, Architecture & Engineering

• Design, develop, test, and support data-driven solutions across on‑prem and AWS environments. 
• Build and maintain modern data lakehouse architectures (e.g., on AWS S3, Apache Iceberg or similar) and cloud data warehouses (e.g., Amazon Redshift). 
• Develop robust ETL/ELT data pipelines for batch and streaming use cases, with a focus on performance, scalability, and reliability. 
• Implement data modelling and transformation layers to support reporting, analytics, and self‑service use cases for Risk and Operations. 
• Enhance platform integration by leveraging domain events and streaming technologies to enable real‑time and near real‑time data processing. 
• Ensure data solutions comply with security, privacy, and regulatory requirements relevant to Risk, Player Sustainability, Finance, Legal, and Regulatory areas. 

Self-Service Analytics & Generative AI

• Collaborate with business stakeholders, analysts, and data scientists to enable self-service analytics using tools such as Power BI and Amazon QuickSight. 
• Create sophisticated reporting and semantic models that deliver compelling narratives and actionable insights. 
• Champion the adoption of generative AI features (e.g., Cursor.ai, Amazon Q, Copilot, and similar tools) to enable intuitive, conversational analytics and accelerate data exploration. 
• Design data products and views optimized for performance, usability, and accessibility by non-technical users. 

Engineering Excellence, Automation & CI/CD

• Improve data engineering processes through automation and standardization, implementing CI/CD pipelines for data pipelines, transformations, and infrastructure. 
• Use and promote open-source data stack tools (e.g., dbt, Apache Airflow, Airbyte) for transformation, orchestration, and ingestion. 
• Apply software engineering best practices (testing, code review, version control, observability) to data solutions. 
• Conduct performance tuning and optimization of queries, pipelines, and data stores to ensure efficient execution at scale. 

Data Governance, Quality & Discovery

• Support and enhance data governance, discovery, and documentation using platforms such as Open Metadata or similar tools. 
• Ensure KPIs, metrics, and data documentation are accurate, complete, and up to date. 
• Contribute to data quality frameworks (validation, testing, monitoring) and ensure adherence to compliance and regulatory standards. 
• Define and track operational metrics and measurements for data products to ensure long‑term health and value. 

Innovation, Collaboration & Operations

• Initiate and experiment with new ideas, tools, and approaches that support the Analytics & Insights (A&I) strategy and the evolution of our hybrid platform. 
• Participate in proof-of-concept (POC) evaluations of tools and technologies; synthesize and present findings to inform leadership decisions. 
• Collaborate early and often on new data initiatives, working across architects and various technical teams to shape and deliver end‑to‑end solutions. 
• Maintain and support data products in production, including release management, incident management, and on‑call support, focusing on optimal long‑term solutions. 
• Build strong relationships with your team and stakeholders, working in an Agile manner with transparent, proactive communication. 

Your experience 

Required

• 5+ years of experience in data engineering, with demonstrated ability to design and implement scalable, flexible modern data architectures. 
• Strong hands-on experience with Oracle databases, including working with an Oracle on-premises data warehouse (data modelling, performance tuning, ETL/ELT, and operations) is mandatory; equivalent strong experience with SQL Server (T-SQL) in a comparable data warehouse environment will also be considered.
• Strong SQL skills, including writing and optimizing complex queries. 
• Experience with ETL/ELT processes and data ingestion from diverse source systems. 
• Proven experience building AWS data pipelines and platforms (e.g., S3, Redshift) for data storage, computation, and security. 
• Proven expertise with data transformation tools such as dbt. 
• Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow) and, ideally, ingestion tools (e.g., Airbyte). 
• Solid understanding of software development best practices and CI/CD processes applied to data (testing, automation, deployment pipelines). 
• Experience with performance tuning and optimization of data pipelines and queries. 
• Strong communication and stakeholder management skills, with the ability to translate business requirements into technical solutions and influence decision-making across teams. 

Advantageous

• Experience building data lakehouse solutions using technologies such as Apache Iceberg or similar table formats. 
• Hands-on experience with BI and visualization tools, such as Power BI and Amazon QuickSight, including semantic modelling and performance optimization. 
• Experience with generative AI applications in analytics and BI (e.g., Amazon Q, Copilot, or equivalent). 
• Familiarity with data discovery and governance tools such as Open Metadata or similar catalog solutions. 
• Experience working within regulated environments (e.g., finance, gaming, legal/regulatory) and knowledge of associated compliance requirements.