Data Engineer

📢 We are looking for a Data Engineer who thrives at the intersection of data engineering, AI, and real-world business impact. In this role, you will design and build production-ready data pipelines and feature engineering workflows that power operational AI, ML, and GenAI use cases on modern data platforms. 

You’ll work closely with clients’ technical teams, architects, and product managers, gaining deep insight into complex data ecosystems and translating them into scalable, secure, and high-quality solutions. This is a hands-on, client-facing role for someone who enjoys ownership, technical depth, and tangible outcomes 💪

✅ Requirements:

• Commercial experience with Palantir 

• 3–5 years of experience in production-grade data engineering

• At least 3 years working with enterprise-class data platforms

• Proven experience designing and deploying large-scale data pipelines

• Strong SQL and Python skills

• Hands-on experience with relational and non-relational databases

• Understanding of ontologies and data platform design patterns

• Strong communication skills and the ability to collaborate with both technical and non-technical stakeholders

✨ Nice to have:

• Experience in the insurance sector

• Background in building client-facing applications, demos, or PoCs

• Experience implementing enterprise security architectures

• Domain exposure to finance, healthcare, or manufacturing

✅ Responsibilities:

• Build robust data pipelines and workflows to prepare data for ML and GenAI use cases

• Implement MLOps/LLMOps tooling for enterprise-scale clients

• Design production-ready, high-quality solutions using modern engineering practices and a pragmatic mindset

• Explore diverse data sources and define efficient, maintainable transformations

• Design ontology models and integration pipelines connecting 5+ systems

• Build demo applications and PoCs that accelerate data-driven business decisions

• Implement security architectures and permission models aligned with enterprise standards

✅ Benefits:

• Work on cutting-edge AI, ML, and GenAI projects with real business impact

• Exposure to complex enterprise environments and top-tier clients

• High level of technical ownership and autonomy

• Opportunity to influence architecture, standards, and best practices

• Collaborative, expert-level team culture with strong knowledge sharing

• Continuous learning support (training, certifications, conferences)

• Flexible working model and a healthy work-life balance

• Competitive compensation aligned with experience and impact