IT Infrastructure - Data Architect

Data Architect

NearU Charlotte Metro (On-site)

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We are hiring a Data Architect to design, build, and evolve our enterprise data platform built on Snowflake and Databricks, with a specific mandate to deliver clean, trusted, AI-ready data that powers analytics, financial reporting, KPI definition, and LLM-driven use cases in Snowflake from Databricks.

NearU is a people-centric, process-driven, and technology-enabled customer service platform dedicated to revolutionizing the home services industry by vastly improving the customer and employee experience.

This role is responsible for data architecture, data quality, and platform reliability. AI outcomes depend on data correctness and consistency, and this architect will ensure data cleansing, normalization, validation, and observability are embedded into every pipeline. The role operates under the direction of the Manager of Data Analytics and IT and partners closely with analytics, finance, operations, and IT leadership.

Objective

Build and maintain a governed Snowflake Databricks data platform where data quality, cleansing, lineage, KPI consistency, and observability are enforced by design, enabling reliable analytics and scalable AI adoption.

Key Responsibilities

Snowflake Architecture & Analytics Enablement

• Architect and maintain Snowflake as the enterprise analytical and AI data backbone
• Design schemas and data models optimized for:
• Financial and operational KPIs
• Executive and self-service analytics
• AI context datasets, features, and embeddings
• Build and maintain Snowflake views that centralize KPI logic and support downstream BI, reporting, and AI workloads
• Implement and govern Streams, Tasks, Dynamic Tables, Snowpark, and secure data sharing
• Optimize warehouse sizing, performance, and cost across BI, transformation, and AI workloads
  
  

Databricks Architecture (Processing, Cleansing & Enrichment)

• Reverse Architect Databricks as the data processing, cleansing, and enrichment layer
• Understand and document current medallion architecture (Bronze / Silver / Gold):
• Bronze: raw, immutable ingestion
• Silver: cleansed, standardized, validated datasets
• Gold: analytics- and AI-ready datasets
• Understand Spark-based cleansing logic including:
• Deduplication and record survivorship
• Data standardization and normalization
• Schema enforcement and drift handling
• Null handling, outlier detection, and anomaly flags
• Ensure reliable and governed handoff of curated data from Databricks into Snowflake
  
  

Data Cleansing, Quality & Standardization

• Define enterprise standards for data quality, completeness, and consistency
• Design reusable cleansing and validation frameworks for structured and unstructured data
• Implement automated data quality checks and scoring at each pipeline stage
• Partner with analytics and business teams to resolve source system inconsistencies
• Ensure analytics and AI datasets are accurate, explainable, and auditable
  
  

KPIs, Snowflake Views & Business Alignment

• Partner with the Manager of Data Analytics and IT to define, document, and operationalize enterprise KPIs
• Translate KPI definitions into governed, reusable Snowflake views for reporting, dashboards, and AI context
• Ensure KPI logic is consistent, traceable to source data, and performant at scale
  
  

ETL / ELT & Data Pipelines

• Design and govern ingestion and ELT pipelines from ERP, SaaS, APIs, and operational systems
• Oversee pipelines built with Fivetran, dbt, and Python
• Embed data cleansing, validation, and reconciliation into ingestion workflows
• Ensure pipelines meet SLAs for analytics, finance, Marketing and AI workloads
  
  

Monitoring, Alerting & Anomaly Detection

• Build monitoring and alerting for Snowflake and Databricks pipelines
• Implement alerts for:
• Pipeline failures and SLA breaches
• Data freshness issues
• Volume and schema anomalies
• Implement anomaly detection for KPI and dataset behavior that may impact reporting or AI outputs
• Partner with IT and Analytics teams on incident response and escalation
  
  

AI & LLM Data Enablement

• Design data architectures that support:
• Retrieval-Augmented Generation (RAG)
• Prompt context datasets
• Embedding generation and lifecycle management
• AI inference feedback loops
• Prepare cleansed, curated datasets that reduce hallucinations and improve AI reliability
• Support integrations with Snowflake Cortex, Azure OpenAI, and related LLM platforms
  
  

Governance, Security & Collaboration

• Enforce data governance, access controls, and masking aligned with enterprise and AI usage policies
• Ensure lineage and transparency from source systems to analytics and AI outputs
• Work under the direction of the Manager of Data Analytics and IT
• Collaborate with data engineers, analysts, and IT teams to deliver production-ready data assets
  
  

Required Qualifications

• 6 years of experience in data architecture, data engineering, or analytics engineering
• Strong hands-on experience with Snowflake
• Experience with Databricks / Apache Spark
• Demonstrated experience designing data cleansing, standardization, and quality frameworks
• Experience supporting AI or LLM-driven data workflows
• Advanced SQL and strong Python skills
• Experience designing enterprise data platforms in a cloud environment (Azure preferred)
  
  

Preferred Qualifications

• Experience with Snowflake Cortex, Snowpark, SQL or AI SQL functions
• Experience enabling RAG pipelines, embeddings, or vector search
• Familiarity with Azure OpenAI or similar LLM platforms
• Experience with data quality or observability tools (e.g., Experian, etc.)
• Experience supporting finance, ERP, or multi-entity data environments
  
  

NearU is an Equal Opportunity Employer AA/EOE/M/F/V/D. In compliance with the Americans with Disabilities Act, NearU may provide reasonable accommodations to qualified individuals with disabilities and encourages both prospective and current employees to discuss potential accommodations with the employer.