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Data Infrastructure And Analytics Jobs in Raleigh, NC

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Data Engineer

Durham, NC · On-site

$57 - $63/hr

This long-term contract opportunity offers remote work flexibility and will focus on developing and optimizing data infrastructure that supports scientific modeling, geospatial analytics, and ...

Data Engineer

Durham, NC

$110K - $132K/yr

As the Data Engineer, you will design and build the data infrastructure that makes Vulcan ... Define data models that support operational queries, analytical workloads, and future AI and ML ...

Data Engineer

Raleigh, NC

$111K - $133K/yr

As the Data Engineer, you will design and build the data infrastructure that makes Vulcan ... Define data models that support operational queries, analytical workloads, and future AI and ML ...

AI Data Engineer

Durham, NC · Hybrid

$110K - $132K/yr

Partner with AI engineers and analysts to enable AI-ready data infrastructure, including support for retrieval-augmented generation, embeddings, and unstructured data ingestion. * Establish platform ...

AI Data Engineer

Durham, NC · On-site

$110K - $132K/yr

Partner with AI engineers and analysts to enable AI-ready data infrastructure, including support for retrieval-augmented generation, embeddings, and unstructured data ingestion. * Establish platform ...

Data Engineer - Manager

Raleigh, NC · On-site

$99K - $232K/yr

Within our Technology Consulting practice, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for ...

Data Engineer

Cary, NC · On-site

$106K - $127K/yr

Knowledge of data modeling, schema design, and indexing strategies for both analytical and AI workloads. * Familiarity with infrastructure-as-code and CI/CD practices for data pipeline deployment (e ...

Data Engineer

Cary, NC

$90K - $150K/yr

Knowledge of data modeling, schema design, and indexing strategies for both analytical and AI workloads. * Familiarity with infrastructure-as-code and CI/CD practices for data pipeline deployment (e ...

Responsibilities - Designing and implementing data infrastructure and systems to facilitate efficient data processing and analysis - Developing and maintaining data pipelines, integration, and ...

... infrastructure, semantic layer, BI platform, and internal AI tooling that powers every insight, automation, and AI-enabled workflow across the company. The team spans data engineering, analytics ...

... infrastructure, semantic layer, BI platform, and internal AI tooling that powers every insight, automation, and AI-enabled workflow across the company. The team spans data engineering, analytics ...

From developing go-to-market strategies and building foundational data analytics infrastructures to leveraging artificial intelligence to improve customer insights and engagement, Beghou helps life ...

Data Engineer

Durham, NC · On-site

$110K - $132K/yr

By owning the entire stack-from sensing hardware to data infrastructure-we have delivered a ... Position Summary The Data Engineer sits within the Data & Analytics organization and supports the ...

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Showing results 1-20

Data Infrastructure And Analytics information

See Raleigh, NC salary details

$148.7K

$173.8K

$196.4K

How much do data infrastructure and analytics jobs pay per year?

As of Jun 13, 2026, the average yearly pay for data infrastructure and analytics in Raleigh, NC is $173,759.00, according to ZipRecruiter salary data. Most workers in this role earn between $160,900.00 and $186,200.00 per year, depending on experience, location, and employer.

Is AI replacing data analysts?

AI is transforming the role of data analysts by automating routine tasks such as data cleaning and basic analysis, allowing analysts to focus on more complex insights and strategic decision-making. While AI tools can augment their work, the need for human expertise in interpreting data, understanding context, and communicating findings remains essential in data infrastructure and analytics roles.

What is the highest paying job in data analytics?

In data analytics, senior roles such as Data Science Director, Chief Data Officer, or Analytics Vice President tend to have the highest salaries, often exceeding six figures annually. These positions require advanced skills in machine learning, data strategy, and leadership, along with extensive experience and often relevant certifications.

What is the difference between Data Infrastructure And Analytics vs Data Engineer?

AspectData Infrastructure And AnalyticsData Engineer
Primary FocusBuilding data systems, managing data infrastructure, and enabling analyticsDesigning, constructing, and maintaining data pipelines and storage solutions
Skills & CertificationsData management, SQL, cloud platforms, analytics toolsProgramming (Python, Java), ETL, database systems, cloud services
Work EnvironmentCollaborates with data analysts, scientists, and business teamsWorks closely with data architects and software engineers
Industry UsageUsed across industries for data-driven decision makingPrimarily in tech, finance, and large enterprises with complex data needs

While both roles involve working with data systems, Data Infrastructure And Analytics focuses on creating and managing the infrastructure that enables data analysis, whereas Data Engineers primarily build and maintain the data pipelines and storage solutions that support these analytics. Understanding these distinctions helps organizations assign the right skills to each role.

What is data infrastructure and analytics?

Data infrastructure and analytics involve building and managing the systems, tools, and processes that collect, store, and analyze data to support business decision-making. Data infrastructure includes databases, data warehouses, and cloud platforms, while analytics involves using statistical and computational methods to extract insights from data. Professionals in this field often work with tools like SQL, Python, and data visualization software to enable data-driven strategies.

Is a data analyst a well paid job?

Data analysts typically earn competitive salaries that vary by industry, experience, and location. Entry-level positions may start lower, but with skills in tools like Excel, SQL, and data visualization, salaries tend to increase with experience and specialization in analytics or business intelligence. Overall, it is considered a well-paying role within the data and technology fields.
What are popular job titles related to Data Infrastructure And Analytics jobs in Raleigh, NC? For Data Infrastructure And Analytics jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Data Infrastructure And Analytics jobs in Raleigh, NC look for? The top searched job categories for Data Infrastructure And Analytics jobs in Raleigh, NC are:
Senior Manager, Data, Analytics & AI

Senior Manager, Data, Analytics & AI

ACA Group

Durham, NC

Other

Posted 1 hour ago


Job description

Senior Manager, Data Analytics and AI - Global Regulatory Advisory will build and scale the data infrastructure, analytics, and AI-enabled automation that power how ACA's Global Regulatory Advisory business operates. Partnering closely with the Head of Operations, Global Regulatory Advisory, and business, finance, and technology leaders across multiple advisory segments, this role will unify fragmented operational, project, client, and financial data into a reliable single source of truth. The role will also deliver executive-ready reporting on utilization, profitability, bookings-to-revenue conversion, and other core KPIs. Success is defined by measurable improvements in data quality, reporting speed and consistency, and the adoption of repeatable, data-led operating rhythms that increase efficiency, margin, and client delivery excellence.

The Opportunity:

ACA Group is building the operational backbone for its next phase of growth-and this role is at the center of it. - Strategy & Operations, you will partner directly with the Head of Operations for ACA's Global Regulatory Advisory business to architect, build, and scale the data infrastructure and analytics capabilities that power how we run, measure, and grow a multi-segment, ~$100M+ advisory practice.

Today, our US Regulatory business spans Private Funds, Fund Officers, Broker Dealer, and Wealth-each with its own delivery model, client base, and operational rhythm. Your mandate is to build and maintain a data and analytics backbone that helps operationalize and systematize how we analyze disparate data at scale, maximize business efficiency and profitability, and create a unified data schema that empowers both practice-level insight and enterprise-level clarity-while maintaining an exceptional client experience for our delivery teams.

You will own the design and implementation of scalable data systems, AI-driven automation, and advanced analytics that enable leadership to manage the business through a single, data-led operating lens-from utilization and scorecard tracking to project profitability, revenue recognition, and management reporting. This is not a support role. This is a builder's role-high autonomy, high impact, and a direct line to the decisions that shape how ACA's largest advisory business operates and scales.

If you're energized by the idea of turning messy, real-world operational data into clean, automated, decision-grade intelligence-and you want to do it inside a fast-moving, PE-backed GRC leader-this is your opportunity.

Job Duties:

  1. Design and build scalable data architecture to unify fragmented product, project, billing, and client engagement data across multiple business segments and legacy systems (PSA tools, CRM, Deltek, Power BI, finance platforms, Workday, manual spreadsheets).

  2. Develop and deploy AI and automation solutions to reduce manual rework in operational workflows, including capacity and utilization reporting, project profitability analysis, data aggregation, cross-functional data migrations, and client engagement hygiene tracking.

  3. Build and maintain advanced dashboards and analytics tools using Power BI, Python, and Excel to deliver real-time, decision-grade visibility into key operational KPIs such as billable utilization, project profitability, cost-to-serve drivers, bookings-to-revenue conversion, and termination/churn analytics.

  4. Integrate disparate data sources across the enterprise (Salesforce, PSA/project management tools, Power BI, SharePoint, finance systems) via APIs and automated ETL pipelines to establish a reliable single source of truth for operational reporting.

  5. Partner cross-functionally with Business Unit Leaders (Private Funds, Fund Officers, Broker Dealer, Wealth), FP&A, Human Resources, and Technology to ensure data definitions, reporting cadences, and KPI frameworks are standardized and aligned to the firm's financial and operational rhythms.

  6. Support the development and automation of management reporting cycles, including quarterly business reviews, monthly financial reviews, and board-level materials-transforming manual, labor-intensive reporting into repeatable, data-driven operating systems.

  7. Quantify operational constraints and opportunities through rigorous analysis-identifying where execution mechanics (data classification errors, billing discrepancies, non-billable capacity inefficiencies) are creating drag on margin and growth, and recommending data-backed improvements.

  8. Prototype and scale AI agents and tools (e.g., Copilot-based automation, custom Python scripts, LLM-assisted workflows) to accelerate reporting, analysis, and decision-making across the US Regulatory team.

  9. Champion a data-led culture within the operations function by establishing SOPs for data governance, reporting standards, and analytics best practices that can be replicated across ACA's global advisory businesses.

Required Education and Experience:

  • Bachelor's degree, preferably in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, Finance, or a related discipline.
  • At least 7-10 years of progressive experience in data analytics, quantitative analysis, business intelligence, or quantitative software/application development, ideally within professional services, financial services, or an advisory environment.
  • Advanced proficiency in Python for data manipulation, automation, and analysis (pandas, NumPy, scripting, API integrations).
  • Proven expertise leveraging AI tools (e.g., Copilot, Claude) in an enterprise environment to drive scalable efficiencies (automation, agent-building, workflow optimization, reporting, QC analysis).
  • Expert-level skills in Microsoft Excel (complex modeling, Power Query, VBA/macros) and Power BI (DAX, data modeling, dashboard design, publishing).
  • Demonstrated experience building APIs and automated data pipelines/ETL processes across enterprise systems.
  • Proven ability to work with large, complex, disparate datasets-cleaning, reconciling, and structuring data into reliable analytical outputs.
  • Experience partnering cross-functionally with senior business leaders, finance teams, and technology stakeholders to translate operational needs into analytical solutions.

Preferred Education and Experience:

  • Experience in a high-growth, private equity-backed, or professional services environment.
  • Familiarity with GRC (governance, risk, and compliance), regulatory advisory, or financial services operations.
  • Hands-on experience with AI/ML tools, large language models, or AI agent development (e.g., Microsoft Copilot, OpenAI APIs, Claude).
  • Experience with Salesforce reporting, PSA/project management platforms, and financial planning tools.
  • Master's degree in a quantitative discipline, MBA, or equivalent advanced training.
  • Prior experience building or scaling a data/analytics function within a complex organization.

Required Skills and Attributes:

  • Builder's mindset: thrives in ambiguity and creates structured, scalable solutions from scratch.
  • Strong operational intuition: connects data to business decisions and understands impact on outcomes.
  • High ownership: takes initiative and drives work from concept through delivery.
  • Cross-functional fluency: effectively translates between business, finance, and technical stakeholders.
  • Precision and rigor: prioritizes data quality, consistency, and analytical integrity.
  • Intellectual curiosity: stays current on emerging tools, especially in AI, automation, and data engineering.
  • Excellent communication skills: able to present complex insights in clear, executive-ready formats.