Sr. AI & Data Engineer

Accenture is hiring for a hybrid Sr. AI & Data Engineer role focused on modern, governed Snowflake data solutions and agentic AI systems on the Snowflake AI Data Cloud.

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 with 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 defined 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 systems and orchestration/governance tooling such as dbt and Apache Airflow, plus 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 using vector search, semantic retrieval, and hybrid search across governed Snowflake datasets.
• Implement tool-use orchestration, prompt engineering, and LLMOps evaluation frameworks to support reliable and auditable agent behavior.
• Apply agentic automation to operational use cases such as anomaly detection, data quality remediation, query diagnosis, documentation generation, and incident summarization.
• Integrate AI agents with enterprise systems via MCP (Model Context Protocol) connectors, REST APIs, and event-driven messaging architectures.
• Define and enforce data modeling standards including Kimball dimensional and Data Vault 2.0 for 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.

Requirements

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

Technology Stack

• Snowflake, Snowflake AI Data Cloud, Snowflake SQL
• Snowpark (Python), Dynamic Tables, Streams, Tasks, Snowpipe
• Snowflake Cortex Agents, Cortex AI Functions, Cortex Search, Cortex Analyst
• Retrieval-Augmented Generation (RAG), vector search, semantic retrieval, hybrid search
• LLMOps, Python, SQL
• RBAC, ABAC, Dynamic data masking, REST APIs, MCP (Model Context Protocol)
• dbt, Apache Airflow, Kimball dimensional, Data Vault 2.0, BI tools

Benefits

• Medical, dental, vision, life, and long-term disability coverage
• 401(k) plan
• Bonus opportunities
• Paid holidays
• Paid time off
• Compensation range varies based on office location, role, skill set, and experience

Location and Schedule

• Irving, TX (hybrid)
• Requires three days per week in the office
• Office locations listed: Dallas, TX; Houston, TX; Columbus, OH; Atlanta, GA; Tampa, FL; Irving, TX; St. Petersburg, FL

Compensation

• Estimated annual salary range: USD 59,100 - 205,800
• Reasonable range varies by location (selected examples provided): California $70,350 to $205,800; Colorado $63,800 to $177,800; Connecticut $63,800 to $177,800; District of Columbia $68,000 to $189,300; Illinois $59,100 to $177,800; Ohio $59,100 to $164,600; Washington $80,200 to $189,300

Preferred Qualifications

• Experience with data modeling methodologies and dbt for transformation layer management
• Familiarity with at least one major cloud platform (AWS, Azure, or GCP) and services (object storage, IAM, networking, managed compute)
• Experience implementing data governance, security policies, and compliance frameworks on cloud data platforms
• 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 production monitoring)
• Familiarity with MCP and enterprise agentic control plane architectures
• Exposure to streaming technologies (e.g., Apache Kafka, Kinesis) integrated with Snowflake ingestion pipelines
• Strong problem solving skills, attention to detail, and ability to work effectively in agile, cross functional teams