Data Engineering

Data Engineering roles:

• Strong hands-on Python engineering skills, including production-grade code, modular design, testing, packaging, and modern tooling (uv/poetry, pydantic, FastAPI, async patterns)
• Practical AI engineering experience - building, integrating, and deploying LLM-powered applications including RAG pipelines, agentic workflows, tool/function calling, prompt engineering, evaluation harnesses, and guardrails
• Working knowledge of major LLM providers and frameworks (Claude, OpenAI, Gemini, open-source models via Bedrock/Vertex/Azure AI, LangChain/LangGraph, LlamaIndex, Haystack) and the trade-offs between them
• Graph database expertise - modelling, querying, and operating graph stores such as Neo4j, Amazon Neptune, or TigerGraph, including Cypher/Gremlin, ontology design, and integrating graphs with vector stores for GraphRAG patterns
• Comfortable across the broader data stack: ingestion and ELT (Airflow, dbt, Fivetran), cloud data platforms (Snowflake, Databricks, BigQuery), vector databases (Pinecone, Weaviate, pgvector), and storage formats (Parquet, Iceberg, Delta)
• Builds robust, observable pipelines with appropriate logging, monitoring, lineage, cost controls, and CI/CD; treats AI components as production software, not notebooks
• Cloud-native delivery on at least one of AWS, Azure, or GCP — including IaC (Terraform), containerisation (Docker), and serverless or Kubernetes-based deployment of AI workloads
• Consulting skills equivalent to a Big 4 environment: structured problem-solving, clear written and verbal communication, stakeholder management, and the ability to lead workshops and present designs to technical and business audiences
• Translates ambiguous client requirements into concrete technical designs, including data models, integration patterns, solution architectures, and effort estimates
• Works directly with client engineering teams and architects, providing uplift on AI engineering best practice, code reviews, and standards
• Collaborates closely with the Leads and offshore delivery teams, owning technical quality of deliverables and ensuring alignment between design intent and implementation
• Stays current on the rapidly evolving AI and data engineering landscape, with a pragmatic view on what is production-ready versus experimental