Data Engineer - ETL

Electronic Arts (EA)
Southam
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Key Responsibilities
  • Involved in entire development lifecycle, from brainstorming ideas to implementing elegant solutions to obtain data insights.
  • Gather requirements, model and design solutions to support product analytics, business analytics and advance data science.
  • Design efficient and scalable data pipelines using cloud‑native and open source technologies.
  • Develop and improve ETL/ELT processes to ingest data from diverse sources.
  • Work with analysts, understand requirements, develop technical specifications for ETLs, including documentation.
  • Support production code to produce comprehensive and accurate datasets.
  • Automate deployment and monitoring of data workflows using CI/CD best practices.
  • Promote strategies to improve data modelling, quality and architecture.
  • Participate in code reviews, mentor junior engineers, and contribute to team knowledge sharing.
  • Document data processes, architecture, and workflows for transparency and maintainability.
  • Work with big data solutions, data modelling, understand the ETL pipelines and dashboard tools.
Required Qualifications
  • 4+ years relevant industry experience in a data engineering role and graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
  • Proficiency in writing SQL queries and knowledge of cloud‑based databases like Snowflake, Redshift, BigQuery or other big data solutions.
  • Experience in data modelling and tools such as dbt, ETL processes, and data warehousing.
  • Experience with at least one of the programming languages Python, C++ or Java.
  • Experience with version control and code review tools such as Git.
  • Knowledge of latest data pipeline orchestration tools such as Airflow.
  • Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure‑as‑code tools (e.g., Docker, Terraform, CloudFormation).
  • Familiarity with data quality, data governance, and observability tools (e.g., Great Expectations, Monte Carlo).
  • Experience with BI and data visualization tools (e.g., Looker, Tableau, Power BI).
  • Experience working with product analytics solutions (Amplitude, Mixpanel).
  • Experience working on mobile attribution solutions (Appsflyer, Singular).
  • Experience working on a mobile game or a mobile app, ideally from early stages of the product life cycle.
  • Experience working in an Agile development environment and familiar with process management tools such as JIRA, Target process, Trello or similar.
Nice to Have
  • Familiarity with data security, privacy, and compliance frameworks.
  • Exposure to machine learning pipelines, MLOps, or AI‑driven data products.
  • Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark.
  • Exposure to AI/ML concepts and collaboration with data science or AI teams.
  • Experience integrating data solutions with AI/ML platforms or supporting AI‑driven analytics.
Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Engineering and Information Technology

Industries

Computer Games


#J-18808-Ljbffr

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.