Sr. AI & Data Engineer – Snowflake

Accenture•St. Petersburg, FL

1d•$54,400 - $205,800•Hybrid

About The Position

The Advanced Technology Centers (ATCs) is the engine for reinvention in our clients’ transformation journey. Powered by more than 255,000 people across 24 countries, ATCs will provide our clients seamless access to industry insights and innovative technology solutions. Stronger together! The Advanced Technology Centers (ATCs) make tremendous impact in solving our clients’ business problems leveraging Innovation, Intelligence, Industry insights, new IT and new technology skills. Now, with the global environment changing at a faster pace, our clients are facing unprecedented challenges and they need us more than ever before. As a Network, ATCs are positioned to unlock greater opportunities and exponential value for our clients. The value for our clients and our people For our clients, the Network provides the strength of our geographic diversity, greater resilience, and seamless access to the deepest industry knowledge, the latest in Gen AI solutions, and tech expertise from around the world. For our people, it brings an opportunity to shape truly boundaryless career paths in a highly collaborative team of experts where they can learn from each other and solve the world’s most complex client challenges. You are: A best in class, hands -on Snowflake Data & Analytics Engineer with deep expertise in building modern, cloud native data solutions. You will design, build, and operationalise enterprise-grade data and agentic AI solutions on the Snowflake AI Data Cloud. You will own the full lifecycle of Snowflake-native data products and agentic AI systems — from architecture and ingestion through to production deployment, governance, and continuous improvement. You are curious, detail oriented, and passionate about writing clean, efficient code. You thrive in fast paced environments and are motivated by solving hard data problems that unlock business value. The Work: There will never be a typical day and that’s why people love it here. The opportunities to make a difference within exciting client initiatives are unlimited in the ever-changing technology landscape. You will be part of a highly collaborative and growing network of technology and data experts, who are taking on today’s biggest, most complex business challenges using the latest data and analytics technologies. We will nurture your talent in an inclusive culture that values diversity. You will have an opportunity to work in a variety of roles covering all aspects of Data Engineering including Data Modelling, Data Integration, Data Analytics, Pipeline Development, Agentic AI, RAG, Cloud and Infrastructure integration, Data Quality, Observability & Governance.

Requirements

A minimum of three years of professional experience in data engineering including experience building agentic AI or LLM-powered applications — including RAG pipelines, tool-use agents, and prompt engineering — in an enterprise context.
A minimum of three years of professional experience with Snowflake-native features: Snowpark (Python), Dynamic Tables, Streams & Tasks, Snowpipe, Cortex AI suite, and performance tuning.
A minimum of three years of experience with Python (data engineering and ML workloads) and advanced SQL.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate Degree, must have minimum 6 years work experience)

Nice To Haves

Experience with data modelling methodologies and dbt for transformation layer management.
Familiarity with at least one major cloud platform (AWS, Azure, or GCP) and associated services (object storage, IAM, networking, managed compute).
Experience implementing data governance, security policies, and compliance frameworks on cloud data platforms.
Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related quantitative discipline; equivalent experience considered.
Strong problem solving skills, attention to detail, and the ability to work effectively in agile, cross functional teams.
Snowflake SnowPro Advanced certification (Data Engineer or Architect) or Cortex AI practitioner credentials.
Experience with vector databases (e.g., Snowflake Vector Search, Pinecone, Weaviate) and embedding models (e.g., Snowflake Arctic Embed).
Hands-on experience with MLOps tooling: model registries, evaluation pipelines, experiment tracking, and monitoring in production.
Familiarity with MCP (Model Context Protocol) and enterprise agentic control plane architectures.
Exposure to streaming technologies (Apache Kafka, Kinesis) integrated with Snowflake ingestion pipelines.

Responsibilities

Architect and maintain a scalable, governed Snowflake AI Data Cloud environment including compute optimisation, warehouse sizing, and cost management.
Design and implement ELT/ETL frameworks using Snowflake SQL, Snowpark Python, Dynamic Tables, Streams, Tasks, and Snowpipe for batch, streaming, CDC, and event-driven ingestion patterns.
Build reusable, AI-ready data products with clear ownership, semantic context, data quality SLAs, and lineage tracking.
Implement enterprise security controls: RBAC/ABAC, column- and row-level security, dynamic data masking, classification tags, and audit logging.
Integrate Snowflake with upstream source systems, orchestration platforms (e.g., dbt, Apache Airflow), governance tools, and downstream analytics consumers.
Design and build multi-step agentic AI workflows using Snowflake Cortex Agents, Cortex AI Functions, Cortex Search, and Cortex Analyst.
Develop production-grade Retrieval-Augmented Generation (RAG) pipelines with vector search, semantic retrieval, and hybrid search capabilities over governed Snowflake datasets.
Implement tool-use orchestration, prompt engineering, and evaluation frameworks (LLMOps) for reliable, auditable AI agent behaviour.
Apply agentic automation to operational use cases including anomaly detection, data quality remediation, query diagnosis, documentation generation, and incident summarisation.
Integrate AI agents with enterprise systems via MCP (Model Context Protocol) connectors, REST APIs, and event-driven messaging architectures.
Define and enforce data modelling standards (Kimball dimensional, Data Vault 2.0) to support analytics, ML feature stores, and AI consumption layers.
Build and maintain semantic data models that expose governed, business-ready datasets to Cortex Analyst and BI tools