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

Apply Now

Data Engineer

83DATA
London
1 day ago
Create job alert

Data Engineer (with Data Analytics Background)

If you are interested in applying for this job, please make sure you meet the following requirements as listed below.

Location: City of London

Employment Type: Full-time

Salary: £90,000-£100,000

Sector: Fintech

Were looking for a well-rounded, communicative Data Engineer with a strong background in data analytics and experience within the Fintech sector. This role is ideal for someone who began their career as a Data Analyst and has since transitioned into a more engineering-focused position, someone who enjoys understanding the business context just as much as building the data solutions behind it.

Youll work extensively with Python, Snowflake, SQL, and dbt to design, build, and maintain scalable, high-quality data pipelines and models that support decision-making across the business. This is a hands-on, collaborative role, suited to someone whos confident communicating with data, product, and engineering teams, not a "heads-down coder" type.

Top 4 Core Skills

Python - workflow automation, data processing, and ETL/ELT development.
Snowflake - scalable data architecture, performance optimisation, and governance.
SQL - expert-level query writing and optimisation for analytics and transformations.
dbt (Data Build Tool) - modular data modelling, testing, documentation, and version control.

Key Responsibilities

Design, build, and maintain dbt models and SQL transformations to support analytical and operational use cases.
Develop and maintain Python workflows for data ingestion, transformation, and automation.
Engineer scalable, performant Snowflake pipelines and data models aligned with business and product needs.
Partner closely with analysts, product managers, and engineers to translate complex business requirements into data-driven solutions.
Write production-grade SQL and ensure data quality through testing, documentation, and version control.
Promote best practices around data reliability, observability, and maintainability.
(Optional but valued) Contribute to Infrastructure as Code and CI/CD pipelines (e.g., Terraform, GitHub Actions).

Skills & Experience

5+ years of experience in data-focused roles, ideally progressing from Data Analyst to Data Engineer.
Proven Fintech or Payments industry experience - strong understanding of the data challenges and regulatory context within the sector.
Deep proficiency in Python, Snowflake, SQL, and dbt.
Excellent communication and collaboration skills, with the ability to work effectively across data, product, and business teams.
Solid grasp of modern data modelling techniques (star/snowflake schemas, data contracts, documentation).
Experience working in cloud-based environments; familiarity with Terraform or similar IaC tools is a plus.
Proactive, delivery-focused, and able to contribute quickly in a fast-moving environment.

Nice to Have

Experience with Power BI or other data visualisation tools.
Familiarity with orchestration tools such as Airflow, Prefect, or Dagster.
Understanding of CI/CD practices in data and analytics engineering.
Knowledge of data governance, observability, and security best practices in cloud environments

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.

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

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.