Senior Data Engineer

Arena Entertainment; City Of London; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading iGaming company in London seeks a Senior Data Engineer to design and optimize modern data pipelines on AWS, supporting analytics across various functions. Ideal candidates will possess AWS expertise, strong SQL skills, and be passionate about data quality. Responsibilities include mentoring junior engineers and ensuring data is reliable for decision-making. Competitive salary and bonus structure offered.

Benefits

Competitive salary

Performance-based bonus

Access to cutting-edge tools and training

Dynamic team culture

Qualifications

• 5+ years of experience as a Senior Data Engineer.
• Proven experience with AWS data services and SQL-based modeling.
• Strong proficiency in dbt and data warehousing best practices.

Responsibilities

• Design and maintain scalable ETL/ELT pipelines.
• Build and optimize data warehouses in Snowflake and/or Redshift.
• Integrate diverse data sources for analytics.

Skills

AWS Data Services

SQL

Data Modeling

Python

dbt

ETL/ELT Pipelines

Tools

Redshift

Snowflake

Airflow

Job description

About the Role

We are looking for a Senior Data Engineer to join our growing analytics and data platform team.
You will be responsible for designing, building, and optimizing modern data pipelines on AWS to support both real time and batch analytics across marketing, acquisition, retention, and product performance with a strong focus on the iGaming and online casino environment.

You’ll work closely with analysts and business stakeholders to ensure high-quality, reliable, and scalable data is available for decision making.

Key Responsibilities

• Design and maintain scalable ETL/ELT pipelines using DMS, dbt, Glue, and Airflow (or similar orchestration tools).
• Build and optimize data warehouses in Snowflake and/or Redshift.
• Integrate diverse data sources (CRM, payment gateways, affiliates, game providers, and marketing platforms).
• Implement data quality, monitoring, and governance standards across the data stack.
• Collaborate with BI, Product, and Marketing teams to deliver clean and trusted data models.
• Support performance tuning, cost optimization, and security within the AWS data ecosystem.
• Mentor junior engineers and participate in data architecture discussions.

Tech Stack

• AWS: Redshift, Glue, Lambda, S3, Athena, Step Functions, EMR, DMS
• Data Modeling / Transformation: dbt, SQL, Python
• Warehousing: Snowflake, Redshift
• Workflow Orchestration: Airflow, AWS Glue, dbt Cloud

Requirements

• 5+ years of experience as a Senior Data Engineer, ideally in a high-volume transactional environment.
• Proven hands-on experience with AWS data services and SQL-based modeling.
• Deep understanding of ELT design principles and data warehousing best practices.
• Expertise in Snowflake or Redshift (query tuning, partitioning, optimization).
• Strong proficiency in dbt (macros, testing, documentation, modularization).
• Good command of Python for data processing and automation.
• Experience with CI/CD and version control (Git) for data workflows.

Nice to Have (iGaming Advantage)

• Familiarity with iGaming KPIs acquisition, retention, RTP, LTV, player segmentation, and responsible gaming metrics.
• Experience with affiliate data platforms, CRM tools, or game provider feeds (e.g., Pragmatic, Evolution, Play’n GO).
• Understanding of real-time data ingestion (DMS, Kafka, Kinesis, Debezium, CDC pipelines).
• Exposure to data science and ML pipelines (SageMaker, Databricks, etc.).
• Quick Suite experience

What We Offer

• Competitive salary and performance-based bonus
• Opportunity to build and scale a modern AWS data stack from the ground up.
• Access to cutting-edge tools, training, and certifications.
• Dynamic, international team culture within a fast-growing iGaming organization.

Improvement Focus

We are continuously improving our data architecture and are looking for someone who can lead these initiatives:

• Reduce Data Lag:
  Migrate to real-time data replication using DMS, Fivetran CDC, Debezium + Kafka, or AWS DMS to achieve low latency.
• Optimize Data Modeling:
  Leverage dbt to transform raw Snowflake data into analytics-ready models, improving maintainability and reducing BI layer load.
• Tune BI Layer(Quick Suite):
  Use SPICE for summaries (with incremental refresh) and Direct Query for real-time dashboards. Implement visual refresh intervals and dataset partitioning for optimal QuickSight performance.