Data Engineer (Data Pipelines & RAG)

Our client is a fast growing Property Tech AI company

About the role

They are seeking a versatile Data & AI Engineer to build, deploy & maintain end-to-end data pipelines for downstream Gen AI applications. You'll design data models and transformations, build scalable ETL/ELT workflows, while learning fast and working on the AI agent space.

Key Responsibilities

Data Modeling & Pipeline development

• Automate data ingestion from diverse sources (Databases, APIs, files, Sharepoint/ document management tools, URLs). Most files are expected to be unstructured documents with different file formats, tables, charts, process flows, schedules, construction layouts/drawings, etc.
• Own chunking strategy, embedding, indexing all unstructured & structured data for efficient retrieval by downstream RAG/agent systems
• Build, test, and maintain robust ETL/ELT workflows using Spark (batch & streaming)
• Define and implement logical/physical data models and schemas. Develop schema mapping and data dictionary artifacts for cross-system consistency

Gen AI Integration

• Instrument data pipelines to surface real-time context into LLM prompts
• Implement prompt engineering and RAG for varied workflows within the RE/Construction industry vertical

Observability & Governance

• Implement monitoring, alerting, and logging (data quality, latency, errors)
• Apply access controls and data privacy safeguards (e.g., Unity Catalog, IAM)

CI/CD & Automation

• Develop automated testing, versioning, and deployment (Azure DevOps, GitHub Actions, Prefect/Airflow)
• Maintain reproducible environments with infrastructure as code (Terraform, ARM templates)

Required Skills & Experience

• 5 years in Data Engineering or similar role, with at least 12-24 months of exposure to building pipelines for unstructured data extraction including document processing with OCR, cloud-native solutions and chunking, indexing etc. for downstream consumption by RAG/ Gen AI applications.
• Proficiency in Python, dlt for ETL/ELT pipeline, duckDB or equivalent tools for analytical in-process analysis, dvc for managing large files efficiently.
• Solid SQL skills and experience designing and scaling relational databases. Familiarity with non-relational column based databases is preferred.
• Familiarity with Prefect is preferred or others (e.g. Azure Data Factory)
• Proficiency with the Azure ecosystem. Should have worked on Azure services in production.
• Familiarity with RAG indexing, chunking and storage across file types for efficient retrieval.
• Strong Dev Ops/Git workflows and CI/CD (CircleCI / Azure DevOps)
• Experience deploying ML artifacts using MLflow, Docker, or Kubernetes is good to have.

Bonus skillsets:

• Experience with Computer vision based extraction or experience in building ML models for production
• Knowledge of agentic AI system design - memory, tools, context, orchestration
• Knowledge of data governance, privacy laws (GDPR) and enterprise security patterns

They are an early-stage startup, so you are expected to wear many hats, working with things out of your comfort zone, but with real and direct impact in production.

Why our client?

• Fast-growing, revenue-generating proptech startup
• Flat, no BS environment, high autonomy for the right talent
• Steep learning opportunities in real world enterprise production use-cases
• Remote work with quarterly meet-ups
• Multi-market, multi-cultural client exposure

Originally posted on Himalayas