AI Native Data Engineer

Who We Are

Build. Scale. Sustain.

PALO IT is a global technology consultancy that crafts tech as a force for good. We design, develop and scale digital and sustainable products and services to unlock value across the triple bottom line: people, planet, profit. We do the right thing, and we do it right.

We're proud to be a World Economic Forum New Champion, and a B Corp-certified company. We are small enough to care locally, big enough to deliver globally (5 continents, 18 offices, +650 experts from +50 nationalities) We are robust and resilient (100% independent and 0 debt) We are entrepreneurs and passionate experts: We invest in what we believe genuinely and work as a collective intelligence We are positive, courageous, caring, doers and committed to excellence About Gen-e2 While the market is still largely AI-augmenting delivery, we have reinvented the SDLC to be AI First. Our approach is a game-changer in productivity and quality, with a strong collaboration between IA generative and our best Talents: We now generate 95% of the entire product — code, documentation, infrastructure as code, and even design — with GitHub Copilot.

The quality consistently exceeds the output of our best traditional engineering teams. A product repository houses all product artefacts, giving AI full project context for higher-quality generation. A library of rules and prompts defines coding standards, design principles, and security guidelines, ensuring enterprise-grade quality and scalability.

With Gen-e2, we deliver end-to-end products 2–3× faster than traditional approaches, while raising the bar for engineering excellence. Your Role As a Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines and architectures that ensure the accessibility, reliability, and quality of data across the organization. You will collaborate closely with data scientists, analysts, and cross-functional teams to transform raw data into actionable insights that drive strategic decisions.

Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data. Build and optimize data lakes and data warehouse solutions in cloud environments. Develop robust data ingestion and transformation processes using Python, SQL, and Scala.

Work with distributed data processing frameworks such as Apache Spark and Hadoop. Orchestrate and monitor workflows using Apache Airflow. Ensure data quality, governance, security, and availability across multiple systems.

Requirements

and deliver high-quality datasets. Optimize database performance and manage large-scale datasets across SQL and NoSQL technologies. Implement best practices for version control, CI/CD, testing, and code quality using Git.

Participate in architecture discussions and contribute to technical decision-making. Support cloud-based data solutions leveraging AWS, GCP, or Azure ecosystems. Continuously improve platform scalability, performance, and operational excellence through automation and AI-assisted engineering practices.

Who You Are

Must Have 3+ years of experience as a Data Engineer or in similar data-focused roles. Strong programming skills in Python and SQL. Experience with big data technologies such as Apache Spark and Hadoop.

Hands-on experience building ETL/ELT pipelines and data integration workflows. Experience working with relational databases such as SQL Server and PostgreSQL. Knowledge of NoSQL databases such as MongoDB or Cassandra.

Experience with cloud platforms such as AWS, GCP, or Azure. Familiarity with Data Warehousing and Data Lake architectures. Experience with workflow orchestration tools such as Apache Airflow.

Strong understanding of version control and collaborative development using Git. Strong analytical thinking and problem-solving skills. Ability to work in agile and collaborative environments.