Senior Data Engineer (AWS)

What do we do?

As a pioneer for digital transformation GFT develops sustainable solutions across new technologies – from cloud engineering and artificial intelligence to blockchain/DLT. With its deep technological expertise, strong partnerships, and comprehensive market know-how GFT offers advice to the financial and insurance sectors, as well as in the manufacturing industry. Through the intelligent use of IT solutions GFT increases productivity and creates added value for clients. Companies gain easy and safe access to scalable IT-applications and innovative business models.

Who are we?

Having started in Germany in 1987, GFT Technologies has grown to become a trusted Software Engineering and Consulting specialist for the international financial industry, counting many of the world’s largest and best-known Banks as our clients. We are an organization that empowers you to not only explore but raise your potential and seek out opportunities that add value. At GFT, diversity, equality, and inclusion are at the core of who we are. Ensuring a diverse and inclusive working environment for all communities is one of the main pillars of our diversity strategy, based on our core values and culture. We have been certified for 2022/23 as a ‘Great place to work’ in the APAC region. So, if you want to have the opportunity to work with an outstanding and progressive organization this position could be right for you.

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.

What can we offer you?

• Competitive salary
• 13th-month salary guarantee
• Performance bonus
• Professional English course for employees
• Premium health insurance
• Extensive annual leave