Data Reporting And Analytics Consultant, Programming

About the role

The Data Reporting and Analytics Consultant designs, develops, and maintains data management, reporting, and analytical solutions that support data-informed decision-making across a wide range of research and business projects. Working under the guidance of senior staff, this role programs in analytical languages such as Python, R, SQL, and Databricks; oversees all phases of data management and quality control; and translates complex data into clear, actionable insights for stakeholders.

Responsibilities

• Program in analytical languages such as Python, SQL, R, or similar tools to support research and business analytics projects.
• Oversee all phases of data management, including data intake, transformation, validation, quality control, and preparation of final analytical datasets.
• Define analytical variables and requirements in collaboration with researchers, statisticians, and other stakeholders to ensure alignment with project goals.
• Perform analytical and statistical tasks using appropriate tools, including descriptive, correlational, inferential, and predictive analyses under the guidance of senior staff.
• Create and maintain interactive Business Intelligence (BI) reports and dashboards that clearly communicate key metrics and insights to stakeholders.
• Develop, implement, and automate business and reporting solutions, including automated data loading schemes, summary statistics, and data visualizations.
• Develop comprehensive quality checking and validation processes, including profiling data issues, cleaning datasets, and monitoring data accuracy and completeness.
• Design and maintain troubleshooting software, scripts, and programs to identify, diagnose, and resolve data and reporting issues.
• Support the interpretation of analytical results by applying findings to real-world contexts and developing clear reports and presentations for decision-makers.
• Consult with internal clients and stakeholders to clarify business questions, translate requirements into analytical plans, and determine appropriate data collection and measurement approaches.
• Develop outcome and process measures to evaluate the impact of business decisions and identify opportunities to improve efficiency through data analysis.
• Summarize data and results in a clear and concise manner, including the creation of summary statistics, tables, and visualizations tailored to different audiences.
• Provide occasional mentoring and technical guidance to colleagues by explaining analytical concepts and demonstrating best practices.

Requirements

• Minimum two (2) years of experience programming with analytical languages such as Python, SQL, R, or similar tools.
• Minimum two (2) years of data management experience using Python, SQL, R, or comparable technologies.
• Minimum two (2) years of experience in data analytics or a directly related field.
• At least 1 year of work experience on research studies.
• Strong proficiency in Python for data management, analysis, and automation.
• Ability to program in R, Python, and Databricks, with a focus on modern analytical workflows.
• Experience with SQL for data extraction, transformation, and querying across relational databases.
• Hands-on experience with data integration, including combining and reconciling data from multiple sources.
• Proficiency in data extraction and data mining techniques to identify patterns, trends, and insights.
• Experience with data visualization tools and techniques to create clear and informative reports and dashboards.
• Working knowledge of statistical programming languages and methods for descriptive, correlational, inferential, and predictive analyses.
• Experience with relational database management concepts and practices.
• Experience with data management and quality control processes.
• Experience with data modeling and testing, including testing, refining, and validating models.
• Experience with data security and privacy practices.

Qualifications

• Bachelor’s degree in Mathematics, Statistics, Engineering, Social Sciences, Physical/Life Sciences, Business, or a related field, or equivalent work experience in lieu of a degree.
• Master’s degree in Mathematics, Statistics, Engineering, Social Sciences, Physical/Life Sciences, Business, Public Health (MPH), or a related field, including MPH with a focus in biostatistics, is preferred.
• Two (2) years of health or medical research experience is preferred.
• Two (2) years of experience working with SQL in complex data environments is preferred.
• Two (2) years of experience working with open-source tools such as R or Python is preferred.
• Familiarity with SAS and SQL / SAS environments, with the ability to work in organizations transitioning away from SAS.
• Experience with Web Report Studio or similar web-based reporting tools.
• Experience developing interactive BI reports and dashboards for diverse stakeholder groups.
• Knowledge of business planning concepts and the ability to align analytical work with strategic objectives.
• Ability to support and train end users on data collection tools, reports, and dashboards.
• Demonstrated ability to influence others through clear technical explanations and practical examples.
• Track record of proactively identifying process improvement opportunities and implementing data-driven solutions.

Skills

• Strong proficiency in Python for data management, analysis, and automation.
• Ability to program in R, Python, and Databricks, with a focus on modern analytical workflows.
• Experience with SQL for data extraction, transformation, and querying across relational databases.
• Hands-on experience with data integration, including combining and reconciling data from multiple sources.
• Proficiency in data extraction and data mining techniques to identify patterns, trends, and insights.
• Experience with data visualization tools and techniques to create clear and informative reports and dashboards.
• Working knowledge of statistical programming languages and methods for descriptive, correlational, inferential, and predictive analyses.
• Experience with relational database management concepts and practices.
• Experience with data management and quality control processes.
• Experience with data modeling and testing, including testing, refining, and validating models.
• Experience with data security and privacy practices.