Data Engineer AI

Hyqoo; Greater London; Hybrid; GBP 85,000 - 120,000; Full time

Job summary

Hyqoo is seeking an AI Data Engineer to design, build, and optimize scalable data solutions that support analytics, AI, and experimentation. You will collaborate with clients and technical teams to understand data needs and transform complex datasets into analysis-ready assets.

Role requires hands-on data engineering experience with big-data technologies and proficiency in Python/SQL, along with strong problem-solving skills and the ability to work in a hybrid environment.

Qualifications

• Hands-on experience building data engineering solutions for AI, ML, or advanced analytics.
• Strong knowledge of big-data technologies, including Hadoop and Spark.
• Proficient in Python, Java, SQL, or Scala.
• Experience with data processing, ETL/ELT, and large-scale data environments.
• Familiarity with analytics tools such as Anaconda or Spark MLlib.

Responsibilities

• Design and develop scalable data solutions and pipelines for AI, machine learning, and advanced analytics.
• Clean, transform, aggregate, and integrate large and complex datasets from multiple sources.
• Design, configure, and maintain database and big-data systems.
• Develop complex SQL queries for data extraction, transformation, validation, and analysis.
• Build and optimize distributed data processing solutions that can scale across multiple machines.
• Implement data quality, reliability, scalability, and disaster-recovery processes.
• Collaborate with data scientists, engineers, analysts, and clients to support AI and advanced analytics initiatives.
• Evaluate and leverage modern big-data and machine-learning technologies to solve complex business problems.

Skills

Data engineering

AI data pipelines

Big-data

ETL/ELT

SQL

Python

Spark

Hadoop

Distributed systems

Data quality

Education

Advanced degree in Computer Science, Data Engineering, or related field

Tools

Hadoop

Spark

Anaconda

Spark MLlib

Job description

YOE: 6+ Years (not leading exp, only hands on Engineering exp)

Duration: 18 months

Hybrid model

We are seeking an AI Data Engineer to design, build, and optimize scalable data solutions that support advanced analytics, AI, and experimentation initiatives. You will work closely with clients and technical teams to understand data requirements, develop robust data pipelines, and transform complex datasets into reliable, analysis-ready assets.

Key Responsibilities

• Design and develop scalable data solutions and pipelines for AI, machine learning, and advanced analytics.
• Clean, transform, aggregate, and integrate large and complex datasets from multiple sources.
• Design, configure, and maintain database and big-data systems.
• Develop complex SQL queries for data extraction, transformation, validation, and analysis.
• Build and optimize distributed data processing solutions that can scale across multiple machines.
• Implement data quality, reliability, scalability, and disaster-recovery processes.
• Collaborate with data scientists, engineers, analysts, and clients to support AI and advanced analytics initiatives.
• Evaluate and leverage modern big-data and machine-learning technologies to solve complex business problems.

Required Qualifications & Skills

• Advanced degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience.
• Hands-on experience building data engineering solutions for AI, machine learning, or advanced analytics.
• Strong knowledge of big-data technologies, including Hadoop and Spark.
• Strong programming skills in Python, Java, SQL, and/or Scala.
• Experience with data processing, ETL/ELT, data transformation, and large-scale data environments.
• Familiarity with advanced analytics and machine-learning tools such as Anaconda, Spark MLlib, or similar technologies.
• Understanding of distributed computing, database architecture, and scalable data platforms.
• Strong analytical and problem-solving skills.

Soft Skills

• Excellent communication and collaboration skills, with the ability to work effectively with both technical teams and clients.
• Strong intellectual curiosity and a willingness to learn and work across new technologies and problem domains.
• Ability to operate effectively in a fast-paced environment and take ownership of complex data challenges.