Snowflake Cloud Engineer

About Our Company Ameriprise India LLP has been providing client based financial solutions to help clients plan and achieve their financial objectives for 20 years. We are part of Ameriprise Financial Inc., a US financial planning company headquartered in Minneapolis with a global presence and diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of year-end 2024. The firm’s focus areas include Asset Management and Advice, Retirement Planning and Insurance Protection. Be part of an inclusive, collaborative culture that rewards you for your contributions, and work with other talented individuals who share your passion for doing great work. You’ll also have plenty of opportunities to make your mark at the office and a difference in your community. So, if you're talented, driven and want to work for a strong, ethical company that cares, take the next step and create a career at Ameriprise India LLP. Job Description Principal Lead Engineer is a key member of an agile team to drive platform enablement, architecture, and technical consulting for our enterprise data platform built on Snowflake and AWS.  The ideal candidate brings deep expertise in Snowflake internals, Agentic AI and best practices, a strong grasp of the AWS ecosystem, and hands-on experience enabling teams to adopt Snowflake and driving implementation of Agentic AI for use-cases within the organization. You will set the technical north star for data and AI-ready design patterns, define platform guardrails and governance, and guide teams to build reliable, secure, and cost‑efficient solutions at scale. Key Responsibilities Platform Strategy & Enablement Serve as the principal consultant for Snowflake best practices, reference architectures, and usage guidelines. Evaluate new Snowflake features (e.g., Snowpark, Dynamic Tables, UniStore, Governance/Access, Performance) and create adoption frameworks, including readiness criteria, rollout plans, and enablement assets. Define AI-ready data patterns (feature stores, embeddings stores, retrieval patterns, prompt/response logging) to accelerate AI solution delivery. Build platform playbooks—workspace patterns, environment strategy, FinOps controls, cost allocation/tagging, quotas, and monitoring standards. Architecture & Design Architect scalable, secure Snowflake environments leveraging a strong understanding of Snowflake internals (services layer, caching, virtual warehouses, optimizer, query lifecycle, micro-partitioning, metadata). Design multi-account AWS integrations (IAM roles, VPC endpoints, PrivateLink, KMS, Secrets Manager, CloudWatch/CloudTrail) and data ingress/egress patterns (S3, Glue, MSK/Kinesis, Lake Formation). Establish performance engineering practices (workload isolation, warehouse sizing/auto-suspend/auto-resume, result caching, query profiling, clustering, materialization strategies). Implement data governance by design: RBAC/ABAC, SSO/OAuth, row/column-level security, masking/tokenization, data retention, lineage/metadata, and auditability. AI Enablement (Cortex AI + Bedrock Agents) Lead enablement for Snowflake Cortex AI capabilities (chat, functions, vector search/embeddings, model invocation), and design secure prompt, context, and evaluation workflows. Guide teams to build agents and RAG solutions using Amazon Bedrock Agents; standardize context retrieval, tool invocation, evaluation metrics, and observability. Define AI governance: model selection, safety policies, red-teaming, usage guidelines, PII handling, evaluation frameworks, prompt/response stores, and responsible AI controls. Create reusable AI accelerators (templates, scaffolds, CI/CD blueprints, example projects) to reduce time‑to‑value. DevEx, Reliability & Operations Establish CI/CD for data and AI (IaC with Terraform/CDK, Snowflake change automation, quality checks with Great Expectations or equivalent). Implement observability across platform and workloads (usage/cost telemetry, query/warehouse metrics, lineage, data SLAs, AI evaluation dashboards). Mentor senior engineers; run design reviews, technical guilds, and architecture councils; uplift teams through workshops and playbooks. Required Qualifications 10–15 years of experience in data engineering/platform engineering with 5+ years in Snowflake, AWS and 2+ years experience in driving AI governance, usage and implementation. Deep understanding of Snowflake architecture: services layer, caching mechanics, virtual warehouses, micro‑partitioning, query lifecycle, optimizer behavior, result/metadata caching. Proven experience creating Snowflake best practices and design patterns (data modeling, workload isolation, cost/perf optimization, governance). Strong AWS expertise for data platforms: S3, IAM, VPC, KMS, CloudWatch/CloudTrail, Glue/Lake Formation, API Gateway/Lambda/ECS, network and security patterns. Demonstrated experience enabling teams on Snowflake Cortex AI (vector search/embeddings, model access, AI functions) and building AI-ready data patterns. Hands-on with Amazon Bedrock Agents (agent orchestration, tool integration, authorization, evaluation, observability). Track record defining AI governance and usage guidelines: responsible AI, data protection, risk controls, auditability, evaluation, and safety. Strong proficiency with SQL, Python, and IaC (Terraform or AWS CDK). Expertise in CI/CD for data & AI workloads, versioning of schemas/objects, automated testing, and change management. Excellent communication and technical leadership skills; ability to influence cross-functional teams and drive platform adoption. Preferred Qualifications Certifications: SnowPro Advanced (or Core) and AWS Solutions Architect/DevOps Engineer. Experience with Snowpark, Dynamic Tables, tasks/streams, external functions, and UDFs/UDTFs. Familiarity with data quality and lineage. Experience with feature stores, RAG architectures, embeddings pipelines, vector DBs (incl. Snowflake Native Vector Search). Knowledge of FinOps for Snowflake and AWS (cost attribution, right‑sizing, guardrails, showback/chargeback). Security background across RBAC/ABAC, OAuth/SCIM/SSO, tokenization/masking, and compliance. Prior experience leading platform product management practices (defining roadmaps, intake & prioritization, stakeholder comms, success metrics). Experience with event streaming (Kafka/MSK/Kinesis), batch & real‑time data patterns, and orchestration (Airflow, Step Functions). Exposure to MLOps: model registries, offline/online feature pipelines, evaluation & monitoring. Full-Time/Part-Time Full time Timings (2:00p-10:30p) India Business Unit AWMPO AWMP&S President's Office Job Family Group Technology Ameriprise India LLP is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, genetic information, age, sexual orientation, gender identity, disability, military status, veteran status, marital status, pregnancy, family status or any other basis prohibited by law. We are committed to fostering an inclusive and accessible recruitment process for individuals with disabilities. If you require a reasonable accommodation to participate in the application or interview process, speak to your recruiter to discuss how we can support you.