Data/AI Engineer

Overview:  

We are looking for a Senior Data Engineer with deep expertise in Lakehouse architecture, real-time data streaming, cloud data infrastructure, and microservices development on Azure Kubernetes Service (AKS). You will play a central role in designing and delivering next-generation data pipelines, BI solutions, AI/ML platforms, streaming APIs, and scalable microservices that power Guidepoint's research and analytics products. 

This is a high-impact, hands-on engineering role. You will work closely with data architects, data scientists, analysts, frontend engineers, QA, and DevOps teams to translate complex business requirements into scalable, reliable, and observable data systems. 

This is a Hybrid role from our Pune office.

What You'll Do:

Data Engineering & Lakehouse  

• Design, build, and maintain ETL pipelines, data ingestion workflows, and table schemas on Azure Databricks to support BI, analytics, and AI/ML use cases 
• Architect and optimize the Lakehouse using Delta Lake on Databricks, ensuring reliability, performance, and cost efficiency 
• Build and support data pipelines from business applications such as Salesforce, NetSuite, and other enterprise systems 
• Develop and maintain Knowledge Graph models, entity relationship structures, and NLP-based insight pipelines 
• Maintain data governance, data privacy standards, and compliance best practices throughout the data lifecycle 
• Perform root cause analysis on data and processes to identify opportunities for improvement 
• Collaborate with data architects, scientists, and business consumers to populate and optimize the data warehouse for reporting and analytics 

Microservices & AKS Development  

• Develop and support scalable web APIs and microservices using Python and Azure Platform Services 
• Build new applications, services, and platforms; optimize existing solutions and refactor legacy components using modern, scalable architectures 
• Design, implement, and deploy microservices on Azure Kubernetes Service (AKS) using Docker, Kubernetes, Helm, and Azure DevOps YAML pipelines 
• Perform end-to-end deployments including infrastructure setup, configuration, and monitoring on AKS 
• Decompose portions of legacy applications into modern microservices architecture 
• Design and manage JSON payloads and payload contexts for inter-service communication 
• Engage in database schema design and management, including updating tables and rows for large datasets 
• Collaborate with cross-functional teams - Full-Stack, QA, DevOps, and Product - in agile SDLC processes 

Real-Time Streaming & SSE  

• Design and implement robust SSE (Server-Sent Events) endpoints using Python frameworks (FastAPI, Flask, Django) for real-time event delivery to web and mobile clients 
• Build and maintain asynchronous backend services using asyncio, aiohttp, or similar libraries for non-blocking, high-concurrency streaming 
• Architect streaming data pipelines integrating SSE with upstream message brokers - Kafka, Redis Pub/Sub, RabbitMQ 
• Optimize connection lifecycle management: reconnection logic, heartbeat signals, event ID tracking, and graceful shutdowns 
• Collaborate with frontend teams to define and evolve SSE event schemas and API contracts 
• Implement observability across streaming services: distributed tracing, structured logging, and metrics using Prometheus, Datadog, or OpenTelemetry 

Engineering Excellence  

• Write comprehensive unit, integration, and load tests for all data, streaming, and microservices components 
• Write and maintain robust CI/CD pipelines using Azure DevOps YAML pipelines 
• Participate in architecture reviews, code reviews, and on-call rotations 
• Maintain thorough technical documentation and mentor junior engineers on best practices in data engineering, Lakehouse architecture, streaming systems, and microservices 

What You Have:

Required  

• Bachelor's degree in Computer Science, Engineering, or a related field from an accredited university 
• 7+ years of professional data engineering and/or backend software engineering experience 
• Advanced SQL expertise across relational and NoSQL databases (SQL Server, Neo4j, Elasticsearch, Cosmos DB) 
• Strong hands-on experience building and optimizing data pipelines on Azure Databricks 
• In-depth knowledge of Delta Lake, Data Warehousing, and Lakehouse architecture 
• Highly proficient in Spark, Python, and SQL 
• Proven experience designing and deploying microservices on AKS using Docker, Kubernetes, and Helm 
• Hands-on experience with Azure DevOps YAML pipelines for CI/CD automation 
• Experience with SSE or real-time streaming - event stream formatting, retry logic, connection management 
• Strong grasp of async Python: asyncio, async/await, event loops 
• Experience with message brokers: Kafka, Redis Streams, RabbitMQ, or similar 
• Proven track record of processing and extracting value from large, complex, and disconnected datasets 
• Excellent stakeholder management and communication skills across global, cross-functional teams 
• Proven leadership skills with a strategic mindset and passion for driving innovation 

Nice to Have  

• Experience with Fivetran for data integration
• Familiarity with BI tools such as Power BI
• Experience building and deploying ML and feature engineering pipelines using MLflow
• Knowledge of Knowledge Graph development (e.g., Neo4j) and NLP-based analytics
• Familiarity with cloud-based AI/ML services and Generative AI tools
• Experience working in a compliance-based environment (building and deploying compliant software throughout the SDLC)
• Familiarity with API gateway configuration for streaming (NGINX, Kong, Azure API Gateway) 

What We Offer: 

• Competitive compensation 
• Employee medical coverage 
• Central office location 
• Entrepreneurial environment, autonomy, and fast decisions 
• Casual work environment 

About Guidepoint :  

Guidepoint powers end-to-end research workflows for the world’s best research teams.

Backed by a global network of more than2 million experts and over 1,600 employees,Guidepoint delivers real-time access to expertise, primary research, and actionable knowledge that help organizations make informed decisions. Through consultations, surveys, events, proprietary content, and AI-enabled tools, we support every stage of the decision-making process-from developing hypotheses and gathering insights to validating assumptions and building conviction for critical decisions.

At Guidepoint, our success relies on the diversity of our employees, experts, and clients, which enables us to foster meaningful connections and a broad range of perspectives. We are committed to creating an inclusive and welcoming environment where individuals of all backgrounds, identities, and experiences can contribute and succeed.