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.

Jobs

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

Careers

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Careers

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.

🍪 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.