IND Staff Engineer, Data

The Hartford; Hyderabad; Hybrid; INR 1,000,000 - 2,100,000; Full time

Job summary

The Hartford in India is seeking an IND Staff Engineer, Data to lead data and AI engineering across a large ecosystem. You will shape real-time pipelines and data products, enabling robust analytics and decisioning across the enterprise.

You will design retrieval-augmented generation, own knowledge graphs, and collaborate with cross-functional teams to scale governance, reliability, and adoption of new technologies.

Qualifications

• Bachelor's or Master's degree in CS/AI.
• Experience with data engineering including SQL/NoSQL, Snowflake, ETL/ELT, CICD, Big Data, and cloud tech (AWS/GCP/Azure).
• Expertise in data architecture, data warehousing, data products and BI.
• Cloud platforms and containers like Docker/Kubernetes experience.
• Experience with Generative AI data pipelines.

Responsibilities

• Lead data and AI engineering for a large data ecosystem across domains and cloud.
• Design and maintain real-time streaming pipelines using Kafka, Kinesis, Spark, or similar.
• Build data and AI pipelines for structured, semi-structured and unstructured data.
• Develop data domains and data products for reporting, data science and AI/ML.
• Implement Retrieval-Augmented Generation architectures with enterprise data infra.
• Collaborate with cross-functional teams to integrate solutions into operations.
• Stay updated on GenAI and prototype experiments; recommend new tools.
• Model domain entities in knowledge graphs and integrate data sources.
• Develop synthetic data workflows and integrate with testing frameworks.
• Improve productivity with AI-driven data engineering and robust CI/CD.

Skills

Data engineering leadership

Streaming data

GenAI integration

Knowledge graphs

Cloud computing

Python/Spark

Snowflake

Kafka

Kubernetes

ETL/ELT

Education

Bachelor's or Master's degree in CS/AI

Tools

SQL

NoSQL

Snowflake

ETL/ELT

CI/CD

Big Data

AWS/GCP/Azure

Python/Spark

Docker

Kubernetes

Job description

IND Staff Engineer, Data - GCC064

We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

Key Responsibilities

• Data and AI Engineering lead for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack
• Real-Time Data Streaming: Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
• Data and AI Engineering lead responsible for Implementing Data and AI pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.
• Develop Data and AI-driven systems to improve data capabilities, ensuring compliance with industry best practices.
• Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
• Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
• Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
• Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems. This includes designing prototypes (POCs) and conduct experiments, and recommend innovative tools and technologies to enhance data capabilities enabling business strategy.
• Model domain entities, relationships, and business logic in knowledge graphs (e.g., Neo4j, Amazon Neptune, RDF). Integrate data from multiple sources, ensuring canonical representation and semantic consistency.
• Synthetic data generation: Develop and validate synthetic data to simulate rare events and edge cases, supporting robust agent evaluation. Integrate synthetic data workflows with automated testing frameworks to ensure consistent, scalable agent performance assessment.
• Identify and Champion AI driven Data Engineering productivity improvements capabilities accelerating end-to-end data delivery lifecycle. This includes researching and implementing innovative solutions such as AI-driven auto-generation of data pipelines, advanced DevOps practices (AI augmented self-healing data pipelines) for data and automated data quality frameworks.
• Semantic layer and Real time analytics: Design and implement scalable semantic layer with dynamic query translation to deliver real time insights for conversational analytics.
• Integrate the semantic layers with AI/LLM platforms to provide low-latency, secure, and context-rich data access, optimized for high concurrency and aligned with enterprise governance standards.
• Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
• Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
• Collaborate closely with DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
• Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
• Develop graph database solutions for complex data relationships supporting AI systems, this also includes developing and optimizing queries (eg Cyhper, SPARQL) to enable complex reasoning, relationship discovery, and contextual enrichment for AI agents.
• Apply GenAI solutions to insurance-specific data use cases and challenges.
• Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment.

Required Skills & Experience :

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field.
• Data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
• Mastery level data engineering and architecture skills, including deep expertise in data architecture patterns, data warehouse, data integration, data lakes, data domains, data products, business intelligence, and cloud technology capabilities.
• Expertise with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Data engineering experience focused on supporting Generative AI technologies.
• Hands on experience with Snowflake
• Experience with building Data and AI pipelines that bring together structured, semi-structured and unstructured data. This includes