As a Lead Data Engineer, 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.
Your skills and experience
Required Skills
12+ years of experience in total, 5+ years as a Data Engineer, hands on Python programming
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.
Why you'll love working here
Our client is a prominent European-based technology consultancy with over 30 years of global expertise in digital transformation. They specialize in engineering next-generation core systems and high-scale data solutions for industry leaders within the Financial Services, Insurance, and Manufacturing sectors. Operating a robust network across more than 20 countries, the firm is at the forefront of integrating advanced automation and modern engineering workflows into the software development lifecycle. With a strong commitment to technical excellence and a distributed global delivery model, they offer senior experts the chance to lead complex modernization initiatives. This is an ideal environment for those looking to influence the future of digital banking and large-scale enterprise architecture.
Why You'll Love Working With Us
Global Innovation Hub: Be part of a prestigious European technology firm with a 30-year legacy, working on large-scale digital transformation projects for top-tier global financial institutions.
Cutting-Edge Tech Stack: Engage with an AI-centric environment where you are encouraged to leverage advanced AI-assisted development tools (like GitHub Copilot and Claude Code) to redefine engineering productivity.
Expert Collaboration: Work alongside some of the industry's brightest minds in an international setting that fosters professional growth and cultural exchange.
Continuous Upskilling: Access specialized training programs, including professional English courses and advanced certifications in Cloud, Architecture, and emerging technologies like Smart Contracts.
Competitive & Transparent Rewards: Enjoy a robust compensation package featuring a guaranteed 13th-month salary, performance-based bonuses, and premium health insurance for your peace of mind.
Work-Life Balance: Benefit from a flexible Hybrid working model and extensive annual leave, allowing you to balance professional excellence with personal well-being.
High-Impact Projects: Direct your expertise toward modernizing next-generation core systems that power the daily lives of millions in the banking and fintech sectors.