Data Engineer

Tiger Analytics
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer (Data Science)

Data Engineer for Data Science — Marketing Analytics

Data Engineer (Data Science)

Data Engineer (Data Science)

Data Engineer, Data Engineer Data Analyst ETL Developer BI Developer Big Data Engineer Analytics Engineer Data Platform Engineer Cloud Data Engineer Azure Data Engineer Data Integration Specialist DataOps Engineer Data Pipeline Engineer

Senior DataOps Engineer

Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, Canada, India, and Singapore, and a substantial remote global workforce.

If you are passionate about working on business problems that can be solved using structured and unstructured data on a large scale, Tiger Analytics would like to talk to you. We are seeking an experienced and dynamic Data Engineer to play a key role in designing and implementing robust data solutions that help in solving the client's complex business problem

Requirements

Responsibilities:

  • Design, develop, and maintain scalable data pipelines using Scala, DBT, and SQL.
  • Implement and optimize distributed data processing solutions using MPP databases and technologies.
  • Build and deploy machine learning models using distributed processing frameworks such as Spark, Glue, and Iceberg.
  • Collaborate with data scientists and analysts to operationalize ML models and integrate them into production systems.
  • Ensure data quality, reliability, and integrity throughout the data lifecycle.
  • Continuously optimize and improve data processing and ML workflows for performance and scalability.

Requirements:

  • 5+ years of experience in data engineering and machine learning.
  • Proficiency in Scala programming language for building data pipelines and ML models.
  • Hands-on experience with DBT (Data Build Tool) for data transformation and modeling.
  • Strong SQL skills for data querying and manipulation.
  • Experience with MPP (Massively Parallel Processing) databases and distributed processing technologies.
  • Familiarity with distributed processing frameworks such as Spark, Glue, and Iceberg.
  • Ability to work independently and collaboratively in a team environment.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

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.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.