AI Native Data Engineer

Who We Are

Building the AI-first frontier enterprise.

We are a global technology consultancy with a trademarked, AI-first approach—Gen-e2™. It redefines how enterprises build digital products and transform their organizations with AI. 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 (10 countries, 450+ experts from 50+ nationalities)

• We are becoming an agentic organization, adopting the AI-native operating model we bring to our clients.

• We are robust and resilient (100% independent, 0 debt, founded 2009)

• We are AI-native professionals who invest in what we believe and work as a collective intelligence

• We are positive, courageous and deliver at the leading edge.

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.

• Collaborate with Data Scientists, Analysts, and Business teams to understand data 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.

• Advanced English communication skills.

Nice to Have

• Experience with Redshift, S3, BigQuery, or Azure Data Factory.

• Knowledge of Infrastructure as Code and DevOps practices.

• Experience working in AI-first or data-driven product environments.

• Exposure to CI/CD pipelines and containerized environments.

• Knowledge of data governance and security best practices.

AI-Native Engineering (Core Expectation)

• Use Generative AI coding tools (e.g., GitHub Copilot, Cursor) as a first-class engineering assistant for:

• Code scaffolding and refactoring

• Code generation and optimisation

• Test-cases and documentation generation

• Build applications through AI-driven development practices, including:

• AI-assisted debugging and troubleshooting

• Intelligent code completion and pattern recognition

• Automated documentation generation

• Apply prompt engineering best practices for reliable, repeatable engineering outcomes.

• Validate GenAI output (determinism checks, guardrails, fallback logic).

More About PALO IT

Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.

What We Offer

• Stimulating working environments

• Unique career path

• International mobility

• Internal R&D projects (including Gen-e2™)

• Knowledge sharing

• Personalized training via PALO IT Academy

• Entrepreneurship & intrapreneurship

For more on our team culture and benefits, check out our careers page.