Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Engineer

Arena Entertainment
City of London
3 days ago
Create job alert
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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.