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

Apply Now

Senior Data Engineer

Cognizant
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineering Consultant

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer (Python/AWS)


Full-time | London, UK


Hybrid working: 2-3 days per week on-site


Must be SC cleared, or eligible to obtain SC-level security clearance


Job description

We are looking for a developer with expertise in Python, AWS Glue, step function, EMR cluster and redshift, to join our AWS Development Team. You will be serving as the functional and domain expert in the project team to ensure client expectations are met.


The role involves structuring analytical solutions that address business objectives and problem solving. We are looking for hands-on experience in writing code for AWS Glue in Python, PySpark, and Spark SQL.


The successful candidate will translate stated or implied client needs into researchable hypotheses, facilitate client working sessions, and be involved in recurring project status meetings. You will develop long-lasting, trusted advisor relationships with clients, bringing an ability to work in dual shore engagement across multiple time zones and manage business uncertainty, day-to-day project operations.


An in-person interview will be required as part of the interview process.


Key responsibilities

  • Hands-on experience in Python programming is essential
  • Responsible for Build, Test and Maintain optimal AWS data pipeline architecture. Building AWS glue, step function, AWS Lambda functions and unit test
  • Work with fellow developers, data architects, data analysts and data scientists on data initiatives
  • Expected to understand the core AWS services and apply best practices regarding security and scalability
  • Understand the current application infrastructure, suggesting changes to it
  • Define and document best practices and strategies regarding application deployment and infrastructure maintenance
  • Define service capacity planning strategies
  • Implement the applications CICD pipeline using the AWS CICD stack
  • Project tasks deliverables and management
  • Deliver on project progress and ensure adherence to client expectation
  • Communications, including deck writing
  • Delivery of output
  • Gathering of client feedback on problem structuring
  • Understand and define business problems, get all background information and collect relevant data points
  • Create solution hypothesis and get client buy in, discuss and align on end objective, staffing need, timelines and budget

Nice to have

  • Hive
  • Pig
  • No-SQL database


#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.

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.