Quantiphi

We are looking for an experienced Data Modeler with strong expertise in Conceptual, Logical, and Physical Data Modeling across enterprise-scale data platforms. The ideal candidate will have hands-on experience designing data solutions for Data Warehouses, Data Lakes, Lakehouses, Data Marts, OLTP, and OLAP systems, along with deep exposure to dimensional and enterprise data modeling methodologies.

The role requires close collaboration with business stakeholders, architects, engineering teams, and leadership to design scalable, high-performance, and governance-driven data ecosystems.

Key Responsibilities

Data Modeling & Architecture

• Design and maintain Conceptual, Logical, and Physical Data Models for enterprise applications.
• Develop scalable models for:
• Data Warehouses
• Data Lakes & Lakehouses
• Data Marts
• OLTP & OLAP systems
• Create ER diagrams using tools such as:
• ERwin
• ER/Studio
• Enterprise Architect
• PowerDesigner
• Implement:
• Entity-Relationship Modeling
• Data Vault Modeling
• Dimensional Modeling
• Suggest optimal modeling approaches based on business requirements and target architecture.
• Define and enforce enterprise data modeling standards and best practices.

Data Management & Governance

• Perform:
• Data Extraction
• Data Analysis
• Data Cleansing
• Data Mapping
• Data Profiling
• Create and maintain:
• Source-to-Target Mapping documents
• Data Dictionaries
• Functional Specifications
• Champion:
• Data Lineage
• Metadata Management
• Data Quality Analysis
• Data Governance standards
• Define data retention policies and automated anomaly detection mechanisms.

Collaboration & Delivery

• Collaborate with:
• Business Stakeholders
• Data Owners
• Business Analysts
• Architects
• Data Engineers
• QA Teams
• Guide teams on:
• Data ingestion logic
• Ingestion frequency
• Data consumption patterns
• Testing strategies
• Monitor project progress and provide updates to leadership on milestones and blockers.
• Mentor junior team members and provide technical guidance on analytics design patterns.

Platform & Engineering Support

• Build scalable ETL/ELT pipelines for large-volume data movement.
• Work with DBAs and Engineering teams to optimize physical data models.
• Support model-driven development and repository management.
• Troubleshoot production data model and service issues.
• Research and recommend modern data management technologies and engineering practices.

Required Skills & Experience

Experience

• 4+ years of experience in enterprise data modeling and analytics environments.
• Experience working with hybrid data ecosystems involving:
• Relational Databases
• Distributed Data Platforms
• Cloud-based Data Systems

Technical Skills

• Strong understanding of:
• Data Warehousing concepts & architecture
• OLTP and OLAP systems
• Data Governance & Data Quality
• Industry Data Models (e.g., ACORD)
• Experience designing complex dimensional data models.
• Exposure to ETL tools and data ingestion frameworks.
• Hands-on experience with cloud platforms:
• AWS
• Azure
• GCP

Data Modeling Tools

Experience with one or more of the following:

• ERwin
• PowerDesigner
• ER/Studio
• Enterprise Architect
• MagicDraw
• Business Glossary tools

Leadership & Communication

• Experience leading large teams and enterprise projects.
• Strong stakeholder management and client-facing experience.
• Excellent verbal and written communication skills.
• Ability to explain complex technical concepts to non-technical stakeholders.
• Strong analytical, troubleshooting, and problem-solving skills.
• Familiarity with Agile methodologies.

Preferred Qualifications

• Experience with Data Vault architecture.
• Experience in insurance industry data models such as ACORD.
• Exposure to metadata-driven and automation-first data engineering practices.
• Experience building semantic layers for BI and analytics solutions.

Work Schedule: 4 days work from office with rotational shifts, including night shifts as per business requirements.

Required Skills:

• Bachelor’s degree in Computer Science or similar field or equivalent work experience
• 3+ years of experience on Data Warehousing, Data Engineering or Data Integration projects
• Expert with data warehousing concepts, strategies, and tools
• Strong SQL background
• Strong knowledge of relational databases like SQL Server, PostgreSQL, MySQL
• Strong experience in GCP & Google BigQuery, Cloud SQL, Composer (Airflow), Dataflow, Dataproc, Cloud Function and GCS
• Good to have knowledge on SQL Server Reporting Services (SSRS), and SQL Server Integration Services (SSIS)
• Good to have a Mainframe skillset
• Experience in Informatica Power exchange for Mainframe, Salesforce, and other new-age data sources
• Experience in integration using APIs, XML, JSONs etc.
• In-depth understanding of database management systems, online analytical processing (OLAP) and ETL (Extract, transform, load) framework, data-warehousing and Data Lakes
• Good understanding of SDLC, Agile and Scrum processes
• Strong problem-solving, multi-tasking, and organizational skills
• Highly proficient in working with large volumes of business data and strong understanding of database design and implementation
• Good written and verbal communication skills
• Demonstrated experience of leading a team spread across multiple locations

Roles & Responsibilities:

• Work with business users and other stakeholders to understand business processes
• Ability to design and implement Dimensional and Fact tables
• Identify and implement data transformation/cleansing requirements
• Develop a highly scalable, reliable, and high-performance data processing pipeline to extract, transform and load data from various systems to the Enterprise Data Warehouse
• Develop conceptual, logical, and physical data models with associated metadata including data lineage and technical data definitions
• Design, develop and maintain ETL workflows and mappings using the appropriate data load technique
• Provide research, high-level design, and estimates for data transformation and data integration from source applications to end-user BI solutions
• Provide production support of ETL processes to ensure timely completion and availability of data in the data warehouse for reporting use
• Analyze and resolve problems and provide technical assistance as necessary
• Partner with the BI team to evaluate, design, develop BI reports and dashboards according to functional specifications while maintaining data integrity and data quality
• Work collaboratively with key stakeholders to translate business information needs into well-defined data requirements to implement the BI solutions
• Leverage transactional information, data from ERP, CRM, HRIS applications to model, extract and transform into reporting & analytics
• Define and document the use of BI through user experience/use cases, prototypes, test, and deploy BI solutions
• Develop and support data governance processes, analyze data to identify and articulate trends, patterns, outliers, quality issues, and continuously validate reports, dashboards and suggest improvements
• Train business end-users, IT analysts, and developers

Required Skills

• 4+ years of hands-on experience in data modeling and business intelligence, with a strong focus on Looker semantic layer development and dashboard creation.
• Deep expertise in LookML, including views, models, explores, and Persistent Derived Tables (PDTs).
• Strong experience in implementing data governance, row-level security, and access filters within Looker.
• Hands-on experience in building, maintaining, and optimizing Looker dashboards and reports.
• Strong proficiency in SQL and mandatory experience working with GCP data platforms such as BigQuery.
• Experience with Git/version control systems for LookML code management and deployments.
• Ability to translate complex business requirements into scalable and reusable LookML models.
• Strong communication and collaboration skills with experience working in global cross-functional teams.

We are looking for a BI professional with strong expertise in Tableau development, analytics, and reporting solutions. The ideal candidate should have hands-on experience in building, maintaining, and optimizing business-critical dashboards while collaborating with business and data teams.

Key Responsibilities:

• Develop, maintain, and optimize Tableau dashboards and reporting solutions across Finance, HR, Operations, and Product domains.
• Manage Tableau Server activities including publishing, performance optimization, and data source management.
• Analyze and resolve dashboard defects, enhancement requests, and data-related issues from stakeholders.
• Perform complex data manipulation, calculations, parameterization, and implement advanced Tableau features.
• Write and optimize SQL queries on GCP data platforms such as BigQuery.
• Use Python scripting for data processing, automation, and backend support related to BI solutions.
• Collaborate with Product Managers, business users, and data engineering teams to deliver scalable reporting capabilities.
• Follow best practices in data warehousing, semantic modeling, version control (Git), and CI/CD workflows.

Required Skills:

• Strong hands-on experience with Tableau Desktop and Tableau Server.
• Expertise in dashboard development, reporting, and performance optimization.
• Mandatory experience working on GCP, especially BigQuery.
• Strong SQL skills with experience optimizing reporting workloads.
• Hands-on experience with Python scripting for BI/reporting support.
• Good understanding of data warehousing and reporting best practices.
• Experience with Git and CI/CD practices for BI deployments.
• Strong analytical, problem-solving, and stakeholder management skills.

This role is responsible for architecting and implementing the Agentic capabilities of the PHI ecosystem. The engineer will lead the development of multi-agent systems, enabling seamless interoperability between AI agents, internal tools, and external services.

The position requires a strong focus on AI safety, secure agent orchestration, and tool-connected AI systems capable of executing complex workflows within the health insurance domain.

1. Agent Orchestration

• Build and manage autonomous AI agents using Agent Development Kit (ADK) and Vertex AI Agent Engine.
• Design and implement multi-agent workflows capable of handling complex tasks.

2. Interoperability

• Implement the Model Context Protocol (MCP) to enable connectivity between:
• AI agents
• Internal PHI tools
• External services and APIs.

3. Multimodal Development

• Build real-time, bidirectional audio applications using the Gemini Live API.
• Integrate image generation models and support multimodal AI capabilities.

4. Safety Engineering

• Implement AI safety layers to protect sensitive healthcare data.
• Use Model Armor and Cloud DLP API to:
• Sanitize prompts
• Prevent exposure of PII/PHI data
• Enforce secure AI interactions.

5. Agent-to-Agent (A2A) Communication

• Configure remote agent connectivity using the A2A SDK.
• Enable cross-agent collaboration and workflow orchestration.

Must-Have Skills

• Advanced proficiency with Agent Development Kit (ADK).
• Strong experience with Vertex AI Agent Engine.
• Hands-on experience with Model Context Protocol (MCP).
• Experience implementing Agent-to-Agent (A2A) workflows using the A2A SDK.
• Expertise in Google Gen AI SDK for Python.
• Experience building multimodal AI applications.
• Proven experience implementing AI safety layers, including:
• Model Armor
• Cloud DLP API

Good-to-Have Skills (Foundation)

Data & Analytics

• BigQuery optimization techniques, including:
• Partitioning
• Clustering
• Denormalization for performance and cost optimization.

Streaming & Real-Time Pipelines

• Experience building real-time data pipelines using:
• Google Pub/Sub
• BigQuery streaming pipelines

We are looking for a hands-on Generative AI Engineer to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units.

This role focuses on developing scalable, reusable AI components across the full stack—covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps—on GCP (primary) and Azure.

This is a core engineering role, not a research or client-facing position.

Key Responsibilities

• Design and build production-ready GenAI systems and platform components
• Develop and deploy RAG pipelines (data ingestion, embeddings, retrieval, APIs)
• Implement agent-based architectures (orchestration, routing, memory, workflows)
• Build and manage LLM infrastructure (model routing, caching, rate limiting, observability)
• Develop scalable APIs and services for AI capabilities
• Implement GenAIOps/MLOps practices (prompt management, evaluation, monitoring, deployment)
• Work with GCP services (Vertex AI, BigQuery, Cloud Run, GKE, Pub/Sub) to deploy solutions
• Ensure AI safety, governance, and compliance (PII protection, guardrails, auditability)
• Collaborate with cross-functional teams to deliver reusable, enterprise-grade solutions

Required Skills & Experience

• Strong hands-on experience in Generative AI and RAG systems (production level)
• Experience building multi-agent or agentic AI systems
• Proficiency in Python and backend/API development
• Hands-on experience with GCP AI/ML ecosystem (Vertex AI, BigQuery, etc.)
• Solid understanding of LLM infrastructure and orchestration layers
• Experience with CI/CD pipelines and Infrastructure as Code (Terraform)
• Knowledge of GenAIOps/MLOps practices and model lifecycle management
• Understanding of AI safety, governance, and compliance

Nice to Have

• Experience with LangChain, LlamaIndex, or similar frameworks
• Familiarity with RAG evaluation tools (RAGAS, DeepEval)
• Knowledge of Knowledge Graphs with RAG
• Experience in multi-cloud environments (GCP + Azure)
• Exposure to BFSI/regulated domains

What We’re Looking For

• Engineers who have built and deployed real-world GenAI systems at scale
• Strong backend and systems-thinking mindset
• Ability to work in fast-paced, enterprise environments

Role Type

• Individual Contributor (IC)
• Platform Engineering / Backend-heavy GenAI role

Build, deploy, and maintain production-grade AI/ML solutions for Fortune 500 enterprise clients on Google Cloud Platform. Hands-on role focused on shipping scalable AI systems across GenAI, agentic workflows, traditional ML, and computer vision.

Key Responsibilities:

Generative AI & Agentic Systems

• Design and build GenAI applications (RAG, agentic workflows, multi-agent systems)
• Develop intelligent systems with memory, planning, and reasoning capabilities
• Implement prompt engineering, context optimization, and evaluation frameworks
• Build observable and reliable multi-agent architectures

Traditional ML & Computer Vision

• Develop ML pipelines (forecasting, recommendation, classification, regression)
• Build production-grade computer vision solutions (document AI, image analysis)
• Perform feature engineering, model optimization, and benchmarking

MLOps & Production Engineering

• Own end-to-end ML lifecycle (CI/CD, testing, versioning, deployment)
• Build scalable APIs, microservices, and data pipelines
• Monitor models, detect drift, and implement A/B testing frameworks

Knowledge Solutions

• Architect knowledge graphs and semantic search systems
• Implement hybrid retrieval (vector + keyword search)

Client Collaboration

• Present technical solutions to enterprise clients
• Collaborate with architects, data engineers, and business teams

Required Skills & Experience

• 3–6 years of hands-on ML Engineering experience
• Strong Python and software engineering fundamentals
• Experience shipping production ML systems on cloud (GCP preferred)
• Experience across GenAI, Traditional ML, Computer Vision
• MLOps experience and RAG-based systems

Preferred

• GCP Professional ML Engineer certification
• Knowledge graphs / semantic search experience
• Experience in regulated industries (Healthcare / BFSI)
• Open-source or technical publications

Role & Responsibilities

• Develop and deliver automation software to build and improve platform functionality
• Ensure reliability, availability, and manageability of applications and cloud platforms
• Champion adoption of Infrastructure as Code (IaC) practices
• Design and build self-service, self-healing, monitoring, and alerting platforms
• Automate development and testing workflows through CI/CD pipelines (Git, Jenkins, SonarQube, Artifactory, Docker containers)
• Build and manage container hosting platforms using Kubernetes

Requirements

• Strong experience deploying and maintaining GCP cloud infrastructure
• Well-versed in service-oriented and cloud-based architecture design patterns
• Knowledge of cloud services including compute, storage, networking, messaging, and automation tools (e.g., CloudFormation/Terraform equivalents)
• Experience with relational and NoSQL databases (Postgres, Cassandra)
• Hands-on experience with automation/configuration tools (Puppet, Chef, Ansible, Terraform)

Additional Skills

• Strong Linux system administration and troubleshooting skills
• Programming/scripting exposure (Bash, Python, Core Java, or Scala)
• CI/CD pipeline experience (Jenkins, Git, Maven, etc.)
• Experience integrating solutions in multi-region environments
• Familiarity with Agile/Scrum/DevOps methodologies

Role & Responsibilities

• Develop and deliver automation software to build and improve platform functionality
• Ensure reliability, availability, and manageability of applications and cloud platforms
• Champion adoption of Infrastructure as Code (IaC) practices
• Design and build self-service, self-healing, monitoring, and alerting platforms
• Automate development and testing workflows through CI/CD pipelines (Git, Jenkins, SonarQube, Artifactory, Docker containers)
• Build and manage container hosting platforms using Kubernetes

Requirements

• Strong experience deploying and maintaining GCP cloud infrastructure
• Well-versed in service-oriented and cloud-based architecture design patterns
• Knowledge of cloud services including compute, storage, networking, messaging, and automation tools (e.g., CloudFormation/Terraform equivalents)
• Experience with relational and NoSQL databases (Postgres, Cassandra)
• Hands-on experience with automation/configuration tools (Puppet, Chef, Ansible, Terraform)

Additional Skills

• Strong Linux system administration and troubleshooting skills
• Programming/scripting exposure (Bash, Python, Core Java, or Scala)
• CI/CD pipeline experience (Jenkins, Git, Maven, etc.)
• Experience integrating solutions in multi-region environments
• Familiarity with Agile/Scrum/DevOps methodologies

We are seeking a skilled Data Engineer to join the AI Platform Capabilities team supporting the UDP Uplift program.

In this role, you will design, build, and test standardized data and AI platform capabilities across a multi-cloud environment (Azure & GCP).

You will collaborate closely with AI use case teams to develop:

• Scalable data pipelines
• Reusable data products
• Foundational data infrastructure

Your work will support advanced AI solutions such as:

• GenAI
• RAG (Retrieval-Augmented Generation)
• Document Intelligence

Key Responsibilities

• Design and develop scalable ETL/ELT pipelines for AI workloads
• Build and optimize data pipelines for structured & unstructured data
• Enable context processing & vector store integrations
• Support streaming data workflows and batch processing
• Ensure adherence to enterprise data models, governance, and security standards
• Collaborate with DataOps, MLOps, Security, and business teams (LBUs)
• Contribute to data lifecycle management for AI platforms

Required Skills

• 5–7 years of hands-on experience in Data Engineering
• Strong expertise in Python and advanced SQL
• Experience with GCP and/or Azure cloud-native data services
• Hands-on experience with PySpark / Spark SQL
• Experience building data pipelines for ML/AI workloads
• Understanding of CI/CD, Git, and Agile methodologies
• Knowledge of data quality, governance, and security practices
• Strong collaboration and stakeholder management skills

Nice-to-Have Skills

• Experience with Vector Databases / Vector Stores (for RAG pipelines)
• Familiarity with MLOps / GenAIOps concepts (feature stores, model registries, prompt management)
• Exposure to Knowledge Graphs / Context Stores / Document Intelligence workflows
• Experience with DBT (Data Build Tool)
• Knowledge of Infrastructure-as-Code (Terraform)
• Experience in multi-cloud deployments (Azure + GCP)
• Familiarity with event-driven systems (Kafka, Pub/Sub) & API integrations

Ideal Candidate Profile

• Strong data engineering foundation with AI/ML exposure
• Experience working in multi-cloud environments
• Ability to build production-grade, scalable data systems
• Comfortable working in cross-functional, fast-paced environments