Lead Data Analyst – Internal Audit

Cencora•Conshohocken, PA

1d•Onsite

About The Position

Internal Audit Data Analytics team is looking for a dedicated team player who shares our passion for data and process improvement to provide analytics support to our Internal Audit organization. This role reports to the Data Analytics Sr. Manager - Internal Audit and will play an integral role in advancing the data analytics goals and skill sets of the Internal Audit (IA) function. This position will actively drive the integration of data analytics and associated data visualizations into internal audit projects to deliver innovative, insightful, and actionable analyses. As a role that provides data visibility across all business and functional units of our enterprise, you will develop reporting/analytics solutions in response to requests of a diverse scope, including, but not limited to risk assessments, audit execution, continuous auditing/monitoring solutions, and special projects.

Requirements

Bachelor’s degree in Accounting, Finance, Business Analytics, Data Analytics, Computer Science, Information Systems, Engineering, Statistics, Mathematics, or another related field; or equivalent work experience.
Minimum of 7-years of professional experience in data analytics, audit analytics, risk management, public accounting, internal audit, compliance, finance analytics, or a related field.
Strong experience retrieving, transforming, analyzing, and validating data using tools and technologies such as SQL, Alteryx, Python, PySpark, Spark, and/or Databricks.
Hands-on experience working in Databricks, including use of notebooks, clusters, jobs/workflows, Delta tables, data pipelines, and collaborative development practices.
Experience applying AI, machine learning, or advanced analytics techniques to business, risk, finance, audit, compliance, operational, or investigative use cases.
Strong experience with business intelligence and data visualization tools such as Power BI, Tableau, Qlik, or similar platforms.
Demonstrated experience designing and building dashboards, scorecards, interactive reports, exception reports, and executive-level analytics summaries.
Experience with data storytelling, including translating complex data analyses into clear insights, business implications, and actionable recommendations.
Strong skills in query development using SQL or equivalent query/scripting tools.
Strong analytical skills in data analysis, statistical/quantitative analysis, data mining, business intelligence, data visualization, and exception reporting.
Working knowledge of Python, PySpark, Spark SQL, or similar programming languages used for data transformation, analytics, and automation.
Knowledge of Databricks capabilities, including notebooks, data engineering workflows, data exploration, Delta Lake concepts, and scalable analytics development.
Ability to build compelling, intuitive, and user-friendly dashboards and interactive reports that support risk identification, audit execution, continuous monitoring, and management decision-making.
Understanding of AI and machine learning concepts, including practical use cases such as anomaly detection, predictive analytics, clustering, classification, text analytics, and natural language processing.
Ability to responsibly and effectively use AI-enabled tools to support analytics development while maintaining appropriate human oversight, validation, data privacy, and governance.
Strong business and financial acumen, including an understanding of functional linkages between processes, risks, controls, systems, and data.
Demonstrated understanding of the internal audit function and the role of Internal Audit as an independent, objective assurance and advisory partner to the business.
Ability to translate audit, risk, and business questions into data requirements, analytical approaches, visualizations, and actionable insights.
Strong data quality and validation mindset, including the ability to assess completeness, accuracy, reasonableness, and reliability of data used in audit analytics.
Strong documentation skills, including the ability to document analytics logic, data sources, assumptions, validation steps, limitations, and conclusions.
Strong interpersonal skills with the ability to work across teams and organizational levels to influence, collaborate, and drive change toward common goals.
Excellent communication skills, both oral and written, with the ability to explain technical concepts to non-technical stakeholders.
Excellent knowledge of Microsoft applications, especially Excel, PowerPoint, Teams, and related productivity tools.
Ability to balance multiple priorities, manage competing deadlines, and deliver high-quality work in a dynamic environment.
Self-starter who is motivated to lead change, improve processes, challenge the status quo, and enhance the Internal Audit department’s data analytics, AI, automation, and reporting capabilities.

Nice To Haves

Experience developing analytics solutions using cloud-based or Lakehouse data environments is preferred.
Experience using AI-enabled tools to improve analytics development, automate data analysis tasks, generate insights, summarize large datasets, or accelerate dashboard and report development is preferred.
Experience with SAP solutions, ERP data, finance data, procurement data, order-to-cash, procure-to-pay, inventory, compliance, or other enterprise process data is preferred.
Experience with robotic process automation, process mining, workflow automation, or related automation technologies is a plus.
Alteryx, Databricks, Power BI, Tableau, SQL, Python, cloud data, audit, risk, or data analytics certification is preferred.
Experience supporting internal audit, compliance, SOX, finance, operational risk, fraud detection, third-party risk, or enterprise risk management analytics.
Experience working with large enterprise datasets and integrating data from multiple systems.
Experience building reusable analytics frameworks, audit testing libraries, continuous monitoring dashboards, or automated exception reporting processes.
Experience with cloud data platforms, data lakes, data warehouses, or Lakehouse architectures.
Familiarity with data governance, data lineage, privacy, security, and access management concepts.
Experience with audit management systems, GRC platforms, ERP systems, or financial reporting systems.
Demonstrated curiosity and interest in emerging technologies, including generative AI, machine learning, process mining, automation, and advanced data visualization.

Responsibilities

Develop analytical scripts, tests and analytical reports using Alteryx and/or SQL, Python, PySpark/Spark based query.
Design, build, and maintain scalable data analytics solutions within Databricks, including notebooks, workflows, data pipelines, and reusable analytics assets to support audit execution and continuous monitoring.
Use AI-enabled tools and techniques to support audit analytics activities, including data profiling, pattern detection, natural language querying, code assistance, anomaly detection, text analytics, and predictive insights where appropriate.
Lead development of ad-hoc/continuous monitoring reporting as needed to support internal investigations, special projects and the on-going monitoring of various risk areas.
Design and deliver dashboards and interactive reports using Power BI, Tableau, or similar business intelligence tools, enabling Internal Audit stakeholders to explore data, monitor risks, identify exceptions, and make informed decisions.
Partner with Internal Audit leadership to increase the effectiveness and efficiency of the function by embedding data analytics, automation, visualization, AI-enabled insights, and continuous monitoring into internal audit processes.
Collaborate with audit teams to define audit objectives, understand business processes and risks, identify data analytics opportunities, document requirements, and develop appropriate analytics approaches.
Engage Internal Audit leadership and stakeholders to present recommendations on new and innovative analytics-based and AI-enabled initiatives.
Identify, evaluate, and obtain access to enterprise data sources required for audit analytics, continuous risk monitoring, dashboard development, and internal audit projects.
Work with structured and unstructured data from multiple enterprise systems, including ERP, finance, operational, compliance, and third-party data sources.
Build repeatable, well-documented, and validated analytics workflows that can be reused across audits, monitoring programs, and business risk areas.
Review project work papers, analytics workflows to ensure proper documentation, validation of data and completion of the project.
Apply appropriate data governance, privacy, security, and access control practices when working with sensitive enterprise data.
Learn, apply, and promote data analytics, AI, automation, and visualization skills among Internal Audit colleagues through knowledge sharing, mentoring, training, and the recommendation of appropriate tools and techniques.
Stay current with emerging technologies, including AI, machine learning, cloud data platforms, Databricks, data visualization tools, automation solutions, and audit analytics trends.