IT Infrastructure - Data Architect

NearU, and Affiliate Regional HVAC Branches-posted 3 days ago

Full-time • Mid Level

Onsite • Charlotte, NC

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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.

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

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)

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

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