Data Engineer

• Develop and maintain Snowflake databases, schemas, tables, views, streams, tasks, and stored procedures.
• Write and optimize complex SQL queries for performance and scalability.
• Design and implement data ingestion and automation solutions using Snowpipe, Streams, Tasks, and scripting.
• Manage Snowflake users, roles, permissions, and Role-Based Access Control (RBAC).
• Optimize performance using clustering, micro-partitioning, query profiling, and warehouse tuning.
• Build integrations with cloud platforms, BI tools, APIs, and external applications.
• Leverage Snowflake Cortex and other latest Snowflake features for AI-driven use cases.
• Troubleshoot production issues and support platform monitoring and optimization.
• Collaborate with data engineers, analysts, and business teams to deliver scalable solutions.

Data Engineer – Snowflake

Data Analyst

• Conduct market and account analysis to identify potential business opportunities, market trends, and competitive intelligence.
• Work with large existing Personal and Commercial lines insurance and broker accounts, understanding their analytics, data management & cloud-related needs and challenges.
• Lead dual-shore teams to deliver targeted analytical use cases and projects in functional areas like Claims, Underwriting, Marketing, Sales and Services, Operations, Finance and Actuarial.
• Work closely with our technical & architecture teams to understand, build and present analytics solutions tailored to each client's requirements.
• Stay up to date with the latest advancements in Analytics, Gen AI, data management technologies and industry trends.
• Collaborate with colleagues in different departments, including sales, marketing, product development, and customer support.
• Build and nurture a strong network and connect with Heads of Business Functions, Data & Analytics Directors and other data analytics professionals.
• Represent the company as a subject matter expert in data management and analytics during industry events, conferences, and webinars.

Senior Engagement Manager – Commercial & Specialty Insurance Analytics

Business Analyst

IT Business Analyst

Software Engineer

• Design, build, and maintain ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake.
• Develop and optimize data ingestion frameworks, data transformations, and end‑to‑end workflows for batch and streaming use cases.
• Implement Delta Lake‑based architectures, including versioning, schema evolution, and ACID‑compliant pipelines.
• Work with stakeholders to understand data requirements and translate them into scalable data engineering solutions.
• Manage and optimize Databricks clusters, jobs, and notebooks for performance and cost efficiency.
• Ensure data quality, reliability, and observability through validation frameworks and monitoring.
• Contribute to data modeling, metadata management, and best practices within the data platform.

Senior Databricks Developer

Executive

• Monthly Tax compliance (Data Extraction, Calculation and Filing), Tax Verification and Tax Adjustments
• Perform Activities within timelines and make sure deliverables meet the predefined KPIs
• Preparation and posting of Journal entries – Adjustments, re-classes etc.
• Perform reconciliation of Balance Sheets accounts, Cash, Tax, Sub-Ledger etc.
• Perform expense Allocations, Re-class entries, Book Adjustments and Accrual entries
• Complete all close related tasks assigned on timely basis adhering to the business quality
• Should have experience in managing a small team - **Applicable only for Senior analysts**

Senior Executive

DevOps Engineer

• Define and own the **DevSecOps and AI Security strategy**, roadmap, and operating model aligned to business and regulatory needs.
• Act as a senior advisor to engineering, platform, AI, and product leaders on **secure architecture and risk trade-offs.**
• Build and scale high-performing DevSecOps and AppSec teams; mentor other team members.
• Represent security in executive forums, client discussions, audits, and risk reviews.
• Lead security integration across **CI/CD pipelines** using Jenkins, GitHub/GitHub Actions, and similar tooling.
• Establish enterprise standards for:
• Secure build pipelines
• Secrets management
• IaC security (Terraform / CloudFormation)
• Dependency and supply-chain security
• Drive adoption of automated security controls: **SAST, DAST, SCA, API security, container scanning.**
• Oversee application security programs including threat modeling, secure design reviews, and vulnerability management.
• Monitor and improve the daily operations and BAU activities.
• Establish guardrails for **AI/GenAI solutions**, including LLM-based apps, RAG pipelines, and agentic workflows.
• Assess and mitigate risks such as prompt injection, data leakage, insecure integrations, model abuse, and third-party AI risk.
• Embed AI security controls into DevSecOps/MLOps pipelines (MLSecOps).
• Translate regulatory and client security requirements into **practical engineering controls.**
• Own security metrics, risk reporting, control evidence, and audit readiness.

Lead Security Engineer – DevSecOps

• Prior experience in all Accounts Payable functions, including Vendor Maintenance, Invoice Processing, Exception handling, Payments & reporting activities of the Accounts Payable
• Processing NON PO/PO Invoices. Enter time-sensitive Utility invoices and research any past due amounts to ensure for current accounts
• Payment processing
• Vendor Master Maintenance
• Vendor Reconciliations
• To ensure SLA targets are achieved
• Month-end responsibilities include ensuring that all invoices are processed by month-end
• Ability to comprehend complex information quickly & respond with clear solutions

Senior Executive – P2P Invoice Processing

• Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).
• Define reference architectures, reusable frameworks, and best practices for LLM applications across the organization.
• Architect and oversee implementation of end-to-end RAG pipelines: Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.
• Drive scalability, reliability, cost optimization, and performance across GenAI platforms.
• Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows.
• Mentor and guide architects, engineers, and data scientists.

Principal Architect, AI Data Engineer

• Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
• Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
• Establish reference architectures, design patterns, and reusable frameworks
• Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
• Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
• Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
• Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
• Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
• Optimise solutions for latency, cost, scalability, and reliability
• Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
• Define engineering best practices: coding standards, testing, packaging, observability
• Ensure seamless integration with enterprise data platforms, APIs, and business applications
• Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
• Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
• Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
• Ensure compliance with data security, privacy, and enterprise governance standards
• Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
• Partner with Data Engineering teams on Data ingestion, pipelines, and quality controls

Architect, AI Data Engineer

• Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
• Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
• Establish reference architectures, design patterns, and reusable frameworks
• Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
• Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
• Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
• Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
• Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
• Optimize solutions for latency, cost, scalability, and reliability
• Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
• Define engineering best practices: coding standards, testing, packaging, observability
• Ensure seamless integration with enterprise data platforms, APIs, and business applications
• Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
• Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
• Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
• Ensure compliance with data security, privacy, and enterprise governance standards
• Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)

• Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
• Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
• Act as the technical anchor for GenAI initiatives across projects
• Drive design reviews, architecture governance, and best practices
• Design and build agentic systems using LLMs for use cases such as knowledge assistants, document automation & intelligence, workflow orchestration
• Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
• Build tool-calling / function-calling frameworks for agent workflows
• Lead end-to-end implementation of RAG pipelines: Data ingestion → chunking → embeddings → vector indexing → retrieval → response generation
• Optimise retrieval quality (recall, relevance, grounding)
• Evaluate and benchmark different architectures
• Develop production-grade APIs/services (FastAPI, Flask, etc.)
• Drive code quality, testing standards, and reusable architecture components
• Ensure solutions are performance optimised (latency, cost, reliability)
• Implement LLM guardrails (hallucination control, safety filters, policy enforcement)
• Define evaluation frameworks (response quality metrics, RAG benchmarking, human-in-the-loop validation)
• Drive end-to-end delivery ownership across multiple projects
• Mentor and guide junior engineers and project teams
• Conduct technical reviews, solution walkthroughs, and code reviews.

Lead AI Data Engineer

Cloud Engineer

• Manage data engineering projects, ensuring alignment with business objectives.
• Provide strategic guidance on data engineering best practices.
• Oversee a team of data engineers.
• Ensure continuous improvement of data processes.
• Design, build, and maintain efficient, reusable, and reliable architecture and code for data pipelines and data applications on AWS.
• Build robust data ingestion pipelines (from on-prem to AWS and within AWS) using AWS services such as Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, and SQS.
• Develop and manage ETL/ELT processes to collect, process, and store data from multiple sources, ensuring data quality, integrity, and security.
• Architect and implement end-to-end data solutions (ingestion, storage, integration, processing, access) on AWS, with a focus on data lakes and data warehouses.
• Participate in the architecture and system design discussions for high-scale data engineering projects.
• Independently perform hands-on development, unit testing, and participate in code reviews to ensure adherence to best practices.
• Implement serverless applications using AWS Lambda, API Gateway, Step Functions, and other AWS technologies.
• Migrate data from traditional relational databases, file systems, and APIs to AWS-based data lakes (S3), RDS, Aurora, and Redshift.
• Implement high-velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred).
• Architect and implement CI/CD strategies for enterprise data platforms.
• Collaborate with product, operations, QA, and cross-functional teams throughout the software development cycle.
• Stay abreast of new technology developments, implement POCs for new tools/technologies, and onboard them for real-world use cases.
• Identify and resolve performance issues and continuously optimize for cost, reliability, and scalability.

Senior AWS Data Engineer

Solutions Engineer

• Lead the design and development of advanced application solutions
• Define best practices for application development and deployment
• Oversee application project execution and resource management
• Ensure alignment of application solutions with business objectives
• Provide technical leadership on application initiatives.

Solution Architect

• Responsible for financial management – including revenue recognition, budgeting, forecasting, contract profit & loss management and customer invoicing
• Prepare variance analysis report and evaluate financial performance by comparing & analyzing actual results with budgets & forecast
• Prepare customer invoices and ensure efficient & accurate processing of invoices without any delay in timeline
• Understanding the contract clauses, deliverable and timelines related to client invoicing
• Liaison with operations and other functional areas to verify Invoice & forecast accuracy
• Process/Functional level data collation and reporting to Internal and External clients.
• Preparation, submission and presentation of various reports for the processes on agreed service level metrics with client.
• Regular maintenance of functional MIS and prepare performance analysis.
• Analyzing data and deriving meaningful results for the use of management in decision-making.
• Ensure accurate, timely and efficient reporting of Weekly, Monthly and Quarterly reports.
• Ensure compliance to client and organizational policies and procedures in respect of reporting.
• Maintain and develop the processes to ensure optimum automation of reports.
• Liaison effectively with leadership team, proving to be an effective and dependable support mechanism.
• Should show sufficient flexibility and urgency to handle pressure in a cross-cultural environment

Executive BSS

• Data modeling (conceptual, logical, physical)
• Designing end-to-end data architectures (ingestion → storage → processing → consumption)
• Defining standards for schemas, naming, versioning, and reuse
• Storage & Processing – relational databases, big data & analytics platforms, object storage
• Cloud Architecture – Azure/Microsoft Fabric fundamentals, cost optimization & scalability patterns, security & networking basics
• Translating business requirements into data structures
• Understanding business processes and operating models
• Explaining complex architectures to non-technical stakeholders
• Creating clear architecture diagrams and decision documents
• Facilitating design workshops and tradeoff discussion

EXL

Data Engineer – Snowflake

Senior Engagement Manager – Commercial & Specialty Insurance Analytics

IT Business Analyst

Senior Databricks Developer

Senior Executive

Lead Security Engineer – DevSecOps

Senior Executive – P2P Invoice Processing

Principal Architect, AI Data Engineer

Architect, AI Data Engineer

Architect, AI Data Engineer

Lead AI Data Engineer

Senior AWS Data Engineer

Solution Architect

Executive BSS

Manager – Azure Data Architect