Top Data & Analytics Jobs in Raleigh-Durham, NC

Provides senior SQL Server database engineering and DBA services for client environments. Responsibilities include performance tuning, cloud database architecture, high availability and disaster recovery, capacity planning, automation, monitoring, migrations, deployment scripting, root-cause analysis, and client advisory services across AWS and Azure.

Provides Tier 1 and Tier 2 remote technical support for hardware, software, peripherals, networks, VPN, and MFA issues. Configures and ships laptops and equipment for new hires, manages accounts and permissions across Google Workspace, Microsoft 365, Slack, and internal systems, and develops knowledge-base documentation and training guides. Supports the company’s North American expansion and remote workforce.

Leads enterprise data governance and stewardship in a federated operating model. Responsibilities include onboarding data domains, implementing metadata and business glossary practices, managing governance platforms, driving stewardship adoption, establishing policies and workflows, monitoring governance KPIs, and partnering with business, technology, security, privacy, legal, and compliance teams. The role also oversees managed service providers and junior governance resources while enabling data quality, compliance, analytics, and AI readiness.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build customer health scores, churn and expansion analytics, retention dashboards, automated alerts, and executive reports. Maintain integrations among databases, CRM, and BI tools while improving data quality and standardizing reporting. Lead AI adoption for analytics, validate generated insights, and enable customer-facing teams to make informed retention and expansion decisions. Requires strong BI, logistics or supply chain analytics, dashboarding, data integration, communication, and customer-focused skills.

Analyze market, industry, competitor, and customer intelligence for Vistex’s served industries. Assess market opportunities, monitor competitors, identify trends, develop competitor profiles, and translate complex information into actionable insights. Findings support go-to-market strategies, product roadmaps, marketing direction, competitive positioning, pipeline development, and revenue growth. The role collaborates with industry, sales, marketing, product, and pre-sales stakeholders while managing multiple projects.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.