Sr. AI & Data Engineer – Snowflake

Accenture•Columbus, OH

1d•$59,100 - $164,600•Hybrid

About The Position

Accenture’s Advanced Technology Centers are a global network delivering technology solutions and industry expertise to clients. The Sr. AI & Data Engineer will design, build, and operationalize enterprise-grade data and agentic AI solutions on the Snowflake AI Data Cloud. This hands-on role owns the full lifecycle of Snowflake-native data products and agentic AI systems, from architecture and ingestion through production deployment, governance, and continuous improvement. The engineer will work across data modeling, integration, analytics, pipeline development, agentic AI, retrieval-augmented generation, cloud and infrastructure integration, data quality, observability, and governance. This is a hybrid role requiring three days per week in the office. Hiring locations are Dallas or Houston, Texas; Columbus, Ohio; Atlanta, Georgia; or Tampa, Florida.

Requirements

At least three years of professional data engineering experience, including enterprise experience building agentic AI or LLM-powered applications such as RAG pipelines, tool-use agents, and prompt engineering.
At least three years of professional experience with Snowflake-native features, including Snowpark (Python), Dynamic Tables, Streams and Tasks, Snowpipe, the Cortex AI suite, and performance tuning.
At least three years of experience with Python for data engineering and ML workloads, and advanced SQL.
Bachelor’s degree or equivalent (minimum 12 years) of work experience; an associate degree requires a minimum of six years of work experience.

Nice To Haves

Experience with data modeling methodologies and dbt for transformation layer management.
Familiarity with at least one major cloud platform (AWS, Azure, or GCP) and related object storage, IAM, networking, and managed compute services.
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 is considered.
Strong problem-solving skills, attention to detail, and 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 such as Snowflake Vector Search, Pinecone, or Weaviate, and embedding models such as Snowflake Arctic Embed.
Hands-on experience with MLOps tooling, including model registries, evaluation pipelines, experiment tracking, and production monitoring.
Familiarity with MCP (Model Context Protocol) and enterprise agentic control plane architectures.
Exposure to streaming technologies such as Apache Kafka or Kinesis integrated with Snowflake ingestion pipelines.

Responsibilities

Architect and maintain a scalable, governed Snowflake AI Data Cloud environment, including compute optimization, 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.
Build reusable, AI-ready data products with clear ownership, semantic context, data quality SLAs, and lineage tracking.
Implement enterprise security controls, including RBAC/ABAC, column- and row-level security, dynamic data masking, classification tags, and audit logging.
Integrate Snowflake with upstream source systems, orchestration platforms such as dbt and 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 RAG pipelines with vector search, semantic retrieval, and hybrid search over governed Snowflake datasets.
Implement tool-use orchestration, prompt engineering, and LLMOps evaluation frameworks for reliable and auditable AI agent behavior.
Apply agentic automation to operational use cases, including anomaly detection, data quality remediation, query diagnosis, documentation generation, and incident summarization.
Integrate AI agents with enterprise systems via MCP connectors, REST APIs, and event-driven messaging architectures.
Define and enforce data modeling standards, including Kimball dimensional modeling and 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.