IND Associate Engineer Data

The Hartford; Hyderabad; On-site; INR 900,000 - 1,300,000; Full time

Job summary

The Hartford in Hyderabad, India seeks an IND Associate Engineer, Data to lead data and AI engineering initiatives across a large data ecosystem. You will design real-time streaming pipelines and develop data products to support AI, analytics and reporting use cases.

You will collaborate with cross-functional teams, implement modern data architectures, and mentor junior engineers while ensuring governance, security and scalability of data platforms.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, or a related field.
• Strong data engineering background covering SQL/NoSQL, Snowflake, ETL/ELT, and cloud technologies.

Responsibilities

• Lead data and AI engineering for large, complex data ecosystems and modern tech stacks.
• Design and maintain real-time data streaming pipelines using Kafka, Kinesis, Spark Streaming, or similar.
• Develop data and AI pipelines combining structured, semi-structured, and unstructured data for AI solutions.
• Build data domains and products for reporting, data science, AI/ML, and analytics.
• Implement Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data.
• Collaborate with cross-functional teams to embed solutions into operations and governance standards.
• Mentor juniors and promote best practices across the team.

Skills

Data engineering

SQL

NoSQL

Snowflake

ETL/ELT

AWS

GCP

Azure

Python

Spark

Kubernetes

Docker

GenAI

RAG

Graph DB

Neo4j

MongoDB

DynamoDB

OpenSearch

Neptune

Education

Bachelor’s degree in Computer Science

Tools

Neo4j

MongoDB

DynamoDB

OpenSearch

Spanner Graph

Neptune

Job description

Job Description:

IND Associate Engineer, Data - GCC061

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 ecosystemleveragingdata domains, data products,cloudand modern technology stack

• Real-Time Data Streaming: Design, build andmaintainscalable and robust real-time data streaming pipelinesusing technologies such as Apache Kafka, AWS Kinesis, Spark streaming,orsimilar.

• 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 productsfor various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.

• Implement efficient Retrieval-Augmented Generation (RAG) architectures andintegratewith 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) andconductexperiments, andrecommendinnovative 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, ensuringcanonicalrepresentation and semantic consistency.

• Synthetic data generation: Develop andvalidatesynthetic 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.

• Identifyand 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 layerwith dynamicquery 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,optimizedfor high concurrency and aligned with enterprise governance standards.

• Ensure the reliability, availability, and scalability of data pipelines and systemsthrough effective monitoring, alerting, and incident management.

• Implement best practices in reliability engineering, including redundancy, fault tolerance,and disasterrecovery strategies.

• Collaborate closely with DevOps and infrastructure teamsto 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 AIsystems,this also includes developing andoptimizingqueries (egCyhper, 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 orMaster's degree in Computer Science, Artificial Intelligence, orrelatedfield.

• Data engineering experience including Datasolutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE),Python/Spark,Datamesh,Datalakeor Data Fabric.

• Mastery level data engineering and architecture skills, including deepexpertisein data architecture patterns, data warehouse, data integration, data lakes, data domains, data products, business intelligence, and cloud technology capabilities.

• Expertisewith 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

• Experiencewith building Data and AI pipelines that bring together structured, semi-structured and unstructured data.This includes pre-processing with extraction, chunking, embedding and grounding strategies, semantic modeling, and getting the data ready for Models and Agentic solutions.

• Strong hands-on experience implementing production ready enterprise grade GenAI data solutions.

• Experiencewith prompt engineering techniques for large language models.

• Experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models.

• Intermediate masteryin processing andleveragingunstructured data for GenAI applications.

• Intermediate mastery inimplementing scalable AI driven data systems supporting agentic solutions (AWS Lambda, S3, EC2,Langchain,Langgraph, MCP, A2A).

• Strong programming skills in Python and familiarity with deep learning frameworks such asPyTorchor TensorFlow.

• Experience in vector databases, graph databases, NoSQL, Document DBs, including design, implementation, and optimization. (e.g., AWS open search, GCP Vertex AI, Neo4j, Spanner Graph, Neptune, Mongo, DynamoDB etc.).

• Masteryin implementing data governancepractices, including Data Quality, Lineage, Data Catalogue capture, holistically, strategically, and dynamically on a large-scale data platform.

• Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders.

• Expert level collaborationskillsacross teams, decision making, conflictresolutionand relationship building skills.

• Expertisein mentoring and developing Junior AI or Data Engineers.

• FamiliarityKnowledge of evolving industry design patterns for AI.

• Strong planning, organization, and execution skills.

• Ability to provide thought leadership to dynamic and collaborativeteams,demonstratingexcellent interpersonal skills and time management capabilities.

• Ability to understand and align deliverables to the departmental andorganizationstrategies andobjectives.

• Ability to lead successfully in a lean, agile, and fast-pacedorganization,leveragingScaled Agile principles and ways of working.

• Leader and team player with a transformation mindset.

• Ability to translate complex technical topics into business solutions and strategies, as well as turn business requirements into a technical solution.

Nice to Have

• Experience in multi cloud hybrid AI solutions.

• Certifications in AI, or GCP or Snowflake

• Experience in P&C or Employee Benefits Insurance industry

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