Role Summary

We are looking for a hands-on Data Engineer with strong Snowflake experience to join our product and solution engineering team. The primary focus of this role is to design and build reliable cloud data platform architectures, data pipelines, data products, and governed analytics solutions on Snowflake across AWS, Azure, and GCP environments.

In addition to core data engineering, the role offers the opportunity to build agentic AI capabilities within Snowflake. This includes developing data-aware agents and multi-agent workflows that use governed enterprise data, models, functions, and tools through Snowflake governance framework, Cortex Agents, and Cortex AI Gateway.

This is a Data Engineering role, not a generalist Applied AI role. We are looking for someone whose foundation is SQL, Python, Snowpark, dbt, data modelling, pipeline engineering, and Snowflake platform implementation, with the technical depth and interest to extend those skills into production-grade agentic systems.

Key Responsibilities

- Design, build, and maintain scalable cloud data platform architectures and data products using Snowflake, SQL, Python, Snowpark, dbt, and cloud object storage.
- Build reliable ingestion pipelines for APIs, files, SFTP, databases, and third-party data sources using Snowflake-native, dbt, and Python-based tooling.
- Develop reusable transformation frameworks and governed data models using Snowflake SQL, Snowpark, and dbt, with data quality controls, orchestration, and monitoring across Bronze, Silver, and Gold layers.
- Configure and work effectively within Snowflake environments, including accounts, databases, schemas, virtual warehouses, roles, resource monitors, network policies, integrations, stages, tasks, streams, secrets, and deployment workflows.
- Implement data governance, discoverability, lineage, access controls, and secure sharing using Snowflake RBAC, tags, masking policies, row access policies, object lineage, and secure data sharing.
- Build data-aware agents and multi-agent workflows within Snowflake that can reason over governed enterprise data and invoke approved models, functions, APIs, and tools.
- Use Cortex Agents, Cortex Analyst, Cortex Search, model services, and Cortex AI Gateway to manage model access, routing, usage controls, observability, and guardrails for agentic applications.
- Operationalise data pipelines and agentic workloads through Git, CI/CD, automated testing, environment promotion,



and production monitoring.
- Work with solution architects, application engineers, data scientists, and client teams to move solutions from prototypes into secure and supportable production deployments.
- Optimise Snowflake workloads for performance, reliability, and cloud cost efficiency.

What We're Looking For

You do not need to be an expert in every AI framework. Strong Data Engineering and Snowflake fundamentals are the priority; agentic AI experience is an key differentiator.

- Approximately 3-6 years of relevant experience in Data Engineering, Data Platform Engineering, or a closely related role.
- Strong hands-on experience with Snowflake in a real development or production environment; familiarity with Snowflake account setup, security, compute, integrations, and administration is essential.
- Strong SQL and Python skills, including advanced SQL development, query profiling and optimisation, and practical experience with Snowpark.
- Experience designing and implementing contemporary cloud data platform architectures using Snowflake, including layered data models and governed consumption patterns.
- Hands-on experience using dbt with Snowflake for modular transformations, testing, documentation, lineage, macros, incremental models, and deployment.
- Experience building production-grade data ingestion, transformation, orchestration, validation, and monitoring pipelines.
- Practical experience with Snowflake governance, including RBAC, databases, schemas, roles, grants, tags, masking policies, row access policies, lineage, and secure sharing.
- Understanding of virtual warehouses, tasks, streams, dynamic tables, stages, file formats, integrations, secrets, service users, resource monitors, and environment promotion.
- Experience with Git and CI/CD practices for data engineering workloads, including automated testing and deployment.
- Experience with at least one cloud platform: AWS, Azure, or GCP, including object storage, identity, networking, and managed data services.
- Strong troubleshooting skills and the ability to work across data, platform, security,



and application boundaries.

Agentic AI Experience

- Hands-on experience building LLM-powered applications, agents, or tool-calling workflows using Python and a suitable framework or SDK.
- Ability to design multi-agent systems with clear orchestration, state, tool permissions, failure handling, human approval points, and observability.
- Experience grounding agents on governed enterprise data through SQL, vector search, retrieval, functions, APIs, or other controlled tools.
- Understanding of model serving, prompt and response logging, evaluation, tracing, token and cost controls, rate limits, and safety guardrails.
- Familiarity with registering and governing models, agents, functions, connections, and tools through Snowflake governance framework, Cortex Agents, and Cortex AI Gateway.
- Ability to distinguish a production agent from a demo by addressing identity, permissions, data access, testing, monitoring, resilience, and operational support.

Nice to Have

- SnowPro Core, SnowPro Advanced Data Engineer, or equivalent practical experience.
- Experience with Snowpark, Snowpipe, Dynamic Tables, Streams and Tasks, Cortex Search, Cortex Analyst, Native Apps, Streamlit in Snowflake, or Snowflake Openflow.
- Experience with Cortex Agents, LangGraph, LangChain, Semantic Kernel, or another orchestration framework used for production agent systems.
- Experience integrating dbt with Snowflake, RBAC and governance policies, Git-based development, and CI/CD; exposure to Databricks, Microsoft Fabric, or Airflow is also useful.
- Experience building SaaS or multi-tenant applications, data APIs, or governed client-facing data products.
- Exposure to Docker, serverless containers, cloud networking, private connectivity, firewalls, and enterprise security requirements.

What Success Looks Like

- You can independently build and troubleshoot a Snowflake data pipeline from ingestion through curated, governed consumption layers.
- You understand how a Snowflake account should be structured, secured, monitored, and cost-controlled, not only how to write SQL queries.
- You can build an agent that uses governed data and tools, and you can explain how its identity, permissions, model access, logging, evaluation, and production support will work.
- You create reusable engineering patterns that help multiple product teams deploy data and agentic solutions faster and more safely.

📌 Data Engineer-Snowflake & Agentic AI (Gurugram)
🏢 EXL
📍 Gurugram