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Data Analytics Jobs in Prospect, KY (NOW HIRING)

LeoForce

Senior Manager Data Analytics

Watson, IN · On-site

$170 - $200/hr

You'll work across Azure Data Factory, Azure Synapse, Microsoft Fabric, Power BI, Oracle, and Salesforce, helping create a more scalable and reliable data environment supporting Finance, Operations ...

Pwc

Data Governance- Manager

Louisville, KY · On-site

$99K - $232K/yr

In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data ...

Ventures Unlimited Inc

Big Data Architect

Louisville, KY · On-site

$61.25 - $78.75/hr

Analyze, design, develop, test and deploy data driven analytics, event driven/ real time analytics, sets of analytics orchestrated through rule engines, analytics that discover and exploit social ...

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How much do data analytics jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for data analytics in Prospect, KY is $50.15, according to ZipRecruiter salary data. Most workers in this role earn between $40.29 and $56.83 per hour, depending on experience, location, and employer.

What is data analytics?

Data analytics is the process of examining raw data to uncover trends, patterns, and insights that can inform decision-making. Professionals in this field use statistical techniques, programming, and data visualization tools to interpret complex data sets. Data analytics is applied in various industries, including business, healthcare, finance, and technology, to optimize operations, improve customer experiences, and drive strategic initiatives. The field often requires knowledge of tools like Excel, SQL, Python, and specialized analytics platforms.

How does a data analytics professional typically collaborate with other departments within an organization?

Data Analytics professionals frequently work alongside teams such as marketing, finance, operations, and product development to identify trends, solve business problems, and inform strategic decisions. Collaboration often involves gathering data requirements, interpreting findings, and presenting actionable insights in a clear and accessible manner. Effective communication and the ability to translate technical data into business terms are essential for ensuring recommendations are implemented and drive measurable impact. Regular cross-functional meetings and project-based teamwork are common, offering opportunities to learn from other disciplines and broaden one's organizational influence.

What are the key skills and qualifications needed to thrive as a data analytics professional, and why are they important?

To thrive as a Data Analytics professional, you need strong quantitative analysis skills, proficiency in statistics, and a relevant degree such as in mathematics, computer science, or a related field. Experience with technical tools like SQL, Python or R, data visualization platforms (e.g., Tableau, Power BI), and sometimes certifications like Google Data Analytics or Microsoft Certified: Data Analyst Associate are highly valuable. Critical thinking, problem-solving, and effective communication are essential soft skills for interpreting data and presenting findings to stakeholders. These skills and qualities are crucial for transforming raw data into actionable insights that drive business decision-making.

What is the difference between Data Analytics vs Data Analyst?

AspectData AnalyticsData Analyst
Role FocusAnalyzing large datasets to identify trends and insightsInterpreting data, creating reports, and supporting decision-making
Skills & CertificationsStatistical skills, data visualization, tools like SQL, Python, RData visualization, Excel, SQL, basic statistical knowledge
Work EnvironmentOften in data teams, tech companies, or consulting firmsBusiness units, marketing, finance, or operations teams
Common UsageRefers to the field or disciplineRefers to the job role or position

While both roles involve working with data, Data Analytics typically refers to the broader field or discipline focused on analyzing data to extract insights. A Data Analyst is a specific job role within that field, responsible for interpreting data, creating reports, and supporting business decisions.

Is a data analyst still a good career?

Data analysts remain in high demand across industries due to the increasing reliance on data-driven decision making. Strong skills in tools like Excel, SQL, and visualization software, along with certifications, can enhance job prospects and career growth in this field.

What jobs can a data analyst do?

A data analyst can work in roles such as business analyst, data scientist, data engineer, or reporting specialist. They analyze data to help organizations make informed decisions, often using tools like Excel, SQL, and visualization software, and may require knowledge of statistical methods and programming languages like Python or R.

What kind of jobs can you get with data analytics?

Data analytics skills can lead to roles such as data analyst, business analyst, data scientist, and data engineer. These jobs involve analyzing data to support decision-making, often requiring proficiency in tools like Excel, SQL, and Python, and may require relevant certifications or a strong understanding of statistical methods.

What job categories do people searching Data Analytics jobs in Prospect, KY look for?

The top searched job categories for Data Analytics jobs in Prospect, KY are:

What cities near Prospect, KY are hiring for Data Analytics jobs?

Cities near Prospect, KY with the most Data Analytics job openings:

Data Analytics jobs near you
Infographic showing various Data Analytics job openings in Prospect, KY as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $104,317 per year, or $50.2 per hour.

Manager of Data & Analytics

ISCO Industries

Louisville, KY • On-site

Full-time

Re-posted 28 days ago

Job description

The Manager of Data & Analytics will serve as the senior leader accountable for ISCO's enterprise data strategy, governance program, analytics capabilities, and AI/ML roadmap. This role owns the end-to-end data value chain - ensuring data is governed, trustworthy, accessible, and actively leveraged to drive operational excellence, strategic decision-making, and competitive advantage.
ISCO is at the early stages of its data maturity journey. The Manager will be expected to stand up foundational governance and data quality capabilities while simultaneously charting the longer-term vision for analytics, AI, and data-driven transformation. This requires a leader who can operate at both the strategic and tactical levels - someone who can present a data strategy to the executive team and also roll up their sleeves to define metadata standards, select tooling, and work through data quality issues on the plant floor.
As a midsize organization, ISCO requires this leader to combine the strategic oversight of a data executive with the hands-on capabilities of a governance architect and lead data steward, particularly in the program's early phases. As the team and program mature, the Manager will shift increasingly toward strategy, stakeholder management, and organizational leadership.
Scope of Accountability
The Manager of Data & Analytics has enterprise-wide accountability spanning:
  • Enterprise Data Strategy: Setting the vision, roadmap, and investment priorities for data, analytics, and AI across ISCO.
  • Data Governance Program: Owning the governance operating model, policy framework, stewardship network, and metadata standards across all priority domains.
  • Master Data Domains: Product, Customer, Supplier, Item/Material, Facilities/Fleet, and Quote data.
  • Operational & Manufacturing Data: Fabrication, labor tracking, work orders, Bills of Materials (BOMs), quality management data (QMDs), and OT/IT integration.
  • Analytics & AI: Business intelligence, advanced analytics, predictive modeling, and AI/ML initiatives enterprise-wide.
  • Cross-Functional Data Integration: Data flowing across operations, sales, quality, finance, and manufacturing systems (ERP, Pipeline, Excel, fabrication systems).
  • Team & Capability Building: The Data & Analytics function including data engineers, analysts, stewards, architects, and data scientists.

Key Responsibilities
  1. Enterprise Data Strategy & Vision
  • Define and own ISCO's enterprise data strategy, aligning data investments with business objectives, the Target Operating Model, and the company's multiyear transformation roadmap.
  • Establish a clear, prioritized, and funded multi-year roadmap for data governance, architecture, analytics, and AI - with measurable milestones and business outcomes.
  • Serve as the executive voice for data across the organization - articulating the value of data to the leadership team and building enterprise-wide commitment to data-driven decision-making.
  • Identify and evaluate emerging technologies, methodologies, and industry trends (e.g., data mesh, data products, generative AI) for applicability to ISCO's context.
  • Develop business cases and ROI frameworks for data investments, ensuring initiatives are tied to measurable value creation.
  1. Establish and Lead ISCO's Enterprise Data Governance Program
  • Launch and mature foundational governance capabilities including:
    • Identifying authoritative "single source of truth" domains.
    • Establishing a data ownership and stewardship model.
    • Implementing data quality controls and a quality framework.
    • Defining governance roles, processes, metadata requirements, and Critical Data Element (CDE) selection.
  • Stand up enterprise-wide policies for data lineage, definitions, data ethics, privacy, security, retention, and lifecycle oversight.
  • Introduce a structured governance operating model spanning Product, Customer, Supplier, Facilities/Fleet, and other critical domains.
  • Develop and maintain a governance policy library, including clear procedures for policy creation, interpretation, enactment, and exception handling.
  • Establish and chair (or co-chair) an enterprise Data & Analytics Governance Board, setting cadence, membership, decision-rights, and escalation paths.
  1. Design and Manage Metadata Frameworks & Knowledge Organization
  • Create and maintain a categorization framework for data assets - including taxonomies, ontologies, business glossaries, and controlled vocabularies - to maximize accessibility and reusability across the enterprise.
  • Structure business metadata in a logical and coherent manner, establishing procedures for updating and modifying definitions and information models in a controlled way.
  • Set standards for the onboarding and linking of technical data assets to business metadata using metadata management solutions.
  • Ensure alignment between business concepts, data models, and technical assets so that information retrieval and data sharing are consistent and reliable.
  • As the team grows, transition hands-on metadata architecture work to a dedicated Governance Architect while retaining strategic oversight and quality assurance of the framework.
  1. Coordinate and Lead Data Stewardship Activities
  • Build and lead the enterprise stewardship network, establishing standard processes for how stewards execute their activities (work steps, tools, communication cadences).
  • Mentor and guide data stewards in stewardship activities including data quality remediation, metadata capture, and business definition maintenance.
  • Interpret governance policies and translate them into actionable guidance for stewards and business users.
  • Provide consolidated reporting on stewardship activities, data quality status, and policy compliance to the governance board and executive leadership.
  • As the program matures, recruit and develop a Lead Data Steward to assume day-to-day stewardship coordination while retaining program-level accountability.
  1. Mature Data Quality & Master Data Management (MDM)
  • Lead MDM/MDG initiatives to improve consistency of product, customer, item/material, quote, and facility data.
  • Address systemic data quality issues identified in operations and manufacturing, such as:
    • Inconsistent data entry causing manual cleanup and undermining repeatability.
    • Fragmented data sources causing discrepancies in labor hours and planning decisions.
    • Lack of accurate labor tracking impacting variance analysis and costing.
  • Drive implementation of enterprise-grade Data Catalog & Data Quality tools (such as Collibra, Alation, Informatica, Atlan, Monte Carlo, Soda) for metadata management and automated quality monitoring.
  • Establish a continuous improvement model for data quality - moving from reactive cleanup to proactive prevention through root-cause analysis, process redesign, and automated controls.
  1. Enable Modern Data Architecture & Data Integration
  • Own the design and evolution of ISCO's enterprise data architecture, ensuring scalable, reliable data systems that align business strategy with IT architecture and future ERP.
  • Identify and prioritize data integration needs across operations, sales, quality, and finance.
  • Drive harmonization of data sources (ERP, Pipeline, Excel, QMD, fabrication systems, etc.) to reduce manual reconciliation and improve accuracy.
  • Provide data architecture leadership for ISCO's ERP modernization initiative, ensuring governance, quality, and integration requirements are embedded in the program from the outset.
  • Evaluate and guide architectural decisions around cloud data platforms, data lakehouse patterns, real-time streaming, and API-based integration.
  1. Build and Lead Analytics, BI, and AI Capabilities
  • Own the enterprise analytics and AI roadmap, including forecasting, predictive quality, anomaly detection, SKU/production optimization, and operational intelligence.
  • Drive modernization of ISCO's BI environment - establishing self-service analytics capabilities, standardized reporting frameworks, and governed data products that business users can trust.
  • Lead real-time manufacturing reporting and alerting through integrated OT/IT data (QMDs, fabrication, work orders).
  • Drive AI/ML initiatives aligned to business needs such as:
    • Demand forecasting and inventory optimization
    • Predictive maintenance and predictive quality
    • Automated process efficiencies
    • Sales/Customer analytics and digital experiences
    • Digital twin and simulation capabilities
  • Establish an AI governance framework - including model validation, bias monitoring, explainability standards, and responsible AI practices - ensuring AI initiatives are trustworthy and aligned with organizational values.
  • Identify and execute quick-win analytics projects that demonstrate value early and build organizational appetite for advanced capabilities.
  1. Organizational Leadership & Team Building
  • Build, lead, and develop a high-performing Data & Analytics function, including data engineers, analysts, data stewards, governance architects, and (over time) data scientists.
  • Define the organizational structure, hiring plan, and capability development roadmap for the D&A team - aligning headcount and skills to the multi-year strategy.
  • Establish a culture of data literacy and data-driven decision-making across the enterprise - through training programs, communications, community-of-practice models, and executive engagement.
  • Develop and manage the D&A budget, including staffing, tooling, infrastructure, and consulting/contractor spend.
  • Create transparent, repeatable processes for ideation, prioritization, intake, delivery, testing, change management, and ROI measurement for all D&A initiatives.
  • Ensure transparency, alignment, and proactive communication in data initiatives - addressing gaps noted in IT's current state (unclear prioritization, lack of strategic direction, inconsistent communication).
  1. Vendor & Partner Management
  • Own vendor relationships for data governance, quality, catalog, analytics, and AI tooling - including evaluation, selection, contract negotiation, and ongoing performance management.
  • Manage relationships with consulting partners, implementation firms, and contract resources supporting the D&A program.
  • Stay current with the vendor landscape and evaluate platform consolidation or expansion opportunities as ISCO's needs evolve.

Key Relationships & Interfaces
This is a highly visible, cross-functional leadership role requiring strong executive presence and collaborative skills.
  • CIO / VP Technology: Direct report. Partners on technology strategy, budget, and organizational alignment. Co-owns the IT-data intersection including ERP modernization and infrastructure.
  • Executive Leadership Team: Presents data strategy, business cases, and program outcomes. Advocates for data investment and builds executive commitment to data-driven transformation.
  • Data & Analytics Governance Board: Chairs or co-chairs the board. Sets agenda, decision-rights, and escalation paths. Drives policy creation and strategic prioritization.
  • Data Stewards (across domains): Provides guidance on standard approaches, mentors on stewardship best practices, ensures consistency across the steward network, and coordinates cross-domain initiatives.
  • Subject Matter Experts (Operations, Manufacturing, Sales, Finance): Engages as partners in defining business rules, data definitions, and domain-specific quality requirements. Leverages SMEs as final arbiters on decisions that cannot be resolved through standard governance channels.
  • IT & Data Engineering: Partners on technical implementation of data catalog, quality tooling, metadata integration, data architecture, and ERP modernization. Provides architectural direction and ensures alignment between data platforms and governance standards.
  • Business Unit Leaders: Aligns governance and analytics priorities with business outcomes; builds trust, adoption, and demand for data capabilities.
  • External Vendors & Partners: Manages tooling vendors, consulting firms, and contract resources.

Tools & Technology
The Manager will evaluate, select, and drive adoption of tools across the following categories:
  • Data Catalog & Metadata Management: e.g., Collibra, Alation, Atlan, Informatica - for managing business glossaries, data lineage, metadata linking, and asset categorization.
  • Data Quality & Observability: e.g., Monte Carlo, Soda, Great Expectations, Informatica Data Quality - for automated quality monitoring, rule enforcement, and incident tracking.
  • BI & Analytics Platforms: e.g., Power BI, Snowflake, Databricks, Microsoft Fabric - for reporting, dashboards, self-service analytics, and advanced analytics.
  • MDM Platforms: As needed to support master data harmonization across ERP and operational systems.
  • AI/ML Platforms: e.g., Databricks ML, Azure ML, SageMaker - for model development, deployment, monitoring, and governance.
  • Data Integration & Orchestration: e.g., Azure Data Factory, Fivetran, dbt - for ETL/ELT, data pipeline orchestration, and source-system integration.

Qualifications
Required
  • 10+ years of progressive experience in data management, governance, analytics, or related disciplines, with at least 3 years in a leadership role managing teams and budgets.
  • Demonstrated experience building and leading an enterprise data governance program, including defining governance roles, policies, operating models, and stewardship networks.
  • Proven track record of delivering enterprise analytics or BI capabilities that drove measurable business outcomes.
  • Experience designing or overseeing metadata frameworks, including business glossaries, taxonomies, or controlled vocabu

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