About GFT

GFT Technologies is an AI-centric global digital transformation company. We design advanced data and AI transformation solutions, modernize technology architectures and develop next-generation core systems for industry leaders in Banking, Insurance, Manufacturing and Robotics. Partnering closely with our clients, we push boundaries to unlock their full potential. With deep industry expertise, cutting-edge technology, and a strong partner ecosystem, GFT delivers responsible AI-centric solutions that combine engineering excellence, high-performance delivery and cost efficiency. Our team of 12,000+ technology experts operates  in 20+ countries worldwide offering career opportunities at the forefront of software innovation. 

Role Summary

As a Senior Data Engineer at GFT, you will be responsible for managing, designing, and enhancing data systems and workflows that drive key business decisions. The role is focused 75% on data engineering, involving the construction and optimization of data pipelines and architectures, and 25% on supporting data science initiatives through collaboration with data science teams for machine learning workflows and advanced analytics. You will leverage technologies like Python, Airflow, Kubernetes, and AWS to deliver high-quality data solutions.

Key Activities: 

• Architect, develop, and maintain scalable data infrastructure, including data lakes, pipelines, and metadata repositories, ensuring the timely and accurate delivery of data to stakeholders. 
• Work closely with data scientists to build and support data models, integrate data sources, and support machine learning workflows and experimentation environments. 
• Develop and optimize large-scale, batch, and real-time data processing systems to enhance operational efficiency and meet business objectives. 
• Leverage Python, Apache Airflow, and AWS services to automate data workflows and processes, ensuring efficient scheduling and monitoring. 
• Utilize AWS services such as S3, Glue, EC2, and Lambda to manage data storage and compute resources, ensuring high performance, scalability, and cost-efficiency. 
• Implement robust testing and validation procedures to ensure the reliability, accuracy, and security of data processing workflows. 
• Stay informed of industry best practices and emerging technologies in both data engineering and data science to propose optimizations and innovative solutions. 

Required Skills: 

• Core Expertise: Proficiency in Python for data processing and scripting (pandas, pyspark), workflow automation (Apache Airflow), and experience with AWS services (Glue, S3, EC2, Lambda). 
• Containerization & Orchestration: Experience working with Kubernetes and Docker for managing containerized environments in the cloud. 
• Data Engineering Tools: Hands-on experience with columnar and big data databases (Athena, Redshift, Vertica, Hive/Hadoop), along with version control systems like Git. 
• Cloud Services: Strong familiarity with AWS services for cloud-based data processing and management. 
• CI/CD Pipeline: Experience with CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline for continuous integration and deployment. 
• Data Engineering Focus (75%): Expertise in building and managing robust data architectures and pipelines for large-scale data operations. 
• Data Science Support (25%): Ability to support data science workflows, including collaboration on data preparation, feature engineering, and enabling experimentation environments. 

Nice-to-have requirements: 

• Langchain Experience: Familiarity with Langchain for building data applications involving natural language processing or conversational AI frameworks. 
• Advanced Data Science Tools: Experience with AWS Sagemaker or Databricks for enabling machine learning environments. 
• Big Data & Analytics: Familiarity with both RDBMS (MySQL, PostgreSQL) and NoSQL (DynamoDB, Redis) databases. 
• BI Tools: Experience with enterprise BI tools like Tableau, Looker, or PowerBI. 
• Messaging & Event Streaming: Familiarity with distributed messaging systems like Kafka or RabbitMQ for event streaming. 
• Monitoring & Logging: Experience with monitoring and log management tools such as the ELK stack or Datadog. 
• Data Privacy and Security: Knowledge of best practices for ensuring data privacy and security, particularly in large data infrastructures. 
• Banking domain experience is preferred, particularly exposure to digital banking, core banking, payments, or financial services projects. 

What we offer you: 
You will be working with some of the brightest people in business and technology on challenging and rewarding projects in a team of like-minded individuals. GFT prides itself on its international environment that promotes professional and cultural exchange and encourages further individual development. 

(Note: Due to the high volume of applications we receive, we are unable to respond to every candidate individually. If you have not received a response from GFT regarding your application within 10 workdays, please consider that we have decided to proceed with other candidates. We truly appreciate your interest in GFT and thank you for your understanding)