Data Engineer - Technical Intelligence

IO Global
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Who are we?

IOHK, is a technology company focused on Blockchain research and development. We are renowned for our scientific approach to blockchain development, emphasizing peer-reviewed research and formal methods to ensure security, scalability, and sustainability. Our projects include decentralized finance (DeFi), governance, and identity management, aiming to advance the capabilities and adoption of blockchain technology globally.

We invest in the unknown, applying our curiosity and desire for positive change to everything we do. By fueling creativity, innovation, and progress within our teams, our products and services are designed for people to be fearless, to be changemakers.

What the role involves:

You will be part of the Technical Intelligence (TechInt) team. The team’s main function is to recon the blockchain industry and feed the company with new trends and projects. The TechInt team has automatized the recon process by utilizing a data lake and machine learning. The team currently harvests data from a variety of different sources. This data gets fed into different systems that then show this data as a report.

You are responsible for maintaining and setting up data solutions and services. A key part would be to aid in the maturation of the data ingestion pipeline and processes. Moreover, you would be expected to create a state-of-the-art data warehouse which would be cloud-native. In your daily job, you do a mix of data engineering, and cloud infrastructure management. 

  • Develop and maintain automated data ingestion (API or crawling) pipelines from source code repositories, social media, and on-chain analytics. 
  • Simplify existing data pipelines - re-architecting where necessary.
  • Research existing datasets to figure out their relevance - and remove irrelevant data pipelines and sources.
  • Design a data warehouse that can be queried by analysts and APIs, and that will serve as a data backend for a reporting web application.
  • Collaborate with data scientists to operationalize ML models and deploy them into production environments.
  • Work closely with leadership to understand and define requirements, ensuring alignment with the department’s strategy and roadmap.
  • Collaborate with a Data Scientist and an Intelligence Engineer to implement technical solutions that meet project goals.
  • Ensure systems are functional, available, and carefully monitored for continuous performance and reliability.

Requirements

Who you are:

  • BSc/MSc in a Computer Science field, or equivalent practical experience. 
  • 4+ years of work experience in data and software engineering : data storage, data manipulation, front-end and back-end development for software applications and data systems.  
  • Preferable work experience with blockchain.
  • Professional English.
  • Knowledge of big data processing platforms (such as Spark, Databricks, Google BigQuery), data orchestration (such as Airflow), and data manipulation libraries (such as Pandas, Polars).
  • Knowledge of cloud services management: AWS services (such as Amazon S3, RDS, Redshift, Lambda, Glue, Athena, DynamoDB) or GCP.
  • Knowledge of relational databases (such as PostgreSQL, Oracle, Mssql) and writing highly optimized SQL.
  • Knowledge of docker container orchestration (such as Kubernetes, ECS).
  • Knowledge of Continuous Integration and Continuous Delivery (CI/CD) pipelines (such as GitHub Actions, Travis, Jenkins).
  • Knowledge of Infrastructure as code (such as Terraform, AWS CloudFormation, AWS CDK).
  • Knowledge of blockchain on-chain data representation.
  • Ability to communicate well both verbally and in writing, with both technical and non-technical partners.


Are you an IOGer?

Do you find yourself questioning the status quo? Do you tinker with ideas and long to turn those ideas into solutions? Are you able to spark thoughtful debates, bringing out the inquisitiveness in others? Does the promise of continuously growing excite you? Then get ready to reimagine everything you thought wasn’t possible because that’s what it means to be an IOGer - we don’t set limits, we break them. 

Benefits

  • Remote work
  • Laptop reimbursement
  • New starter package to buy hardware essentials (headphones, monitor, etc)
  • Learning & Development opportunities
  • Competitive PTO 

At IOG, we value diversity and always treat all employees and job applicants based on merit, qualifications, competence, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.