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Data Analytics Jobs in Abingdon, MD (NOW HIRING)

Quevera

Data Analytics Software Engineer

Linthicum, MD · On-site

$129 - $153/hr

Data Analytics Software Engineer Full Time Technical 4 days ago Requisition ID: 3370 Salary Range: $129,485.00 To $152,744.00 Annually Quevera is seeking a Data Analytics Software Engineer with an ...

Under Armour, Inc.

Sr. Data Analytics Engineer

Baltimore, MD · On-site +1

$125K - $165K/yr

As a Senior Data Engineer, you will design and implement data analytics pipelines, develop high-quality data models, and enable automation that supports advanced analytics and AI use cases. You will ...

Under Armour

Sr. Data Analytics Engineer

Baltimore, MD · On-site +1

$125K - $165K/yr

As a Senior Data Engineer, you will design and implement data analytics pipelines, develop high-quality data models, and enable automation that supports advanced analytics and AI use cases. You will ...

Index Analytics

Data Analyst

Baltimore, MD · On-site +1

$75K - $110K/yr

Index Analytics, LLC, is a rapidly growing, Baltimore-based small business providing health-related ... POSITION OVERVIEW The Data Analyst will support data analysis, reporting, and analytics solution ...

Index Analytics LLC

Data Analyst

Baltimore, MD · On-site

$70 - $95/hr

Index Analytics, LLC, is a rapidly growing, Baltimore-based small business providing health-related ... POSITION OVERVIEWThe Data Analyst will support data analysis, reporting, and analytics solution ...

INDEX ANALYTICS LLC

Data Analyst

Baltimore, MD

Index Analytics, LLC, is a rapidly growing, Baltimore-based small business providing health-related ... POSITION OVERVIEW The Data Analyst will support data analysis, reporting, and analytics solution ...

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Data Analytics information

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How much do data analytics jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for data analytics in Abingdon, MD is $55.11, according to ZipRecruiter salary data. Most workers in this role earn between $44.28 and $62.45 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 Abingdon, MD look for?

The top searched job categories for Data Analytics jobs in Abingdon, MD are:

What cities near Abingdon, MD are hiring for Data Analytics jobs?

Cities near Abingdon, MD with the most Data Analytics job openings:

Data Analytics jobs near you
Infographic showing various Data Analytics job openings in Abingdon, MD as of August 2026, with employment types broken down into 82% Full Time, 7% Part Time, 7% Temporary, and 4% Contract. Highlights an 89% In-person, 7% Hybrid, and 4% Remote job distribution, with an average salary of $114,625 per year, or $55.1 per hour.

Senior Data Analyst / Researcher

Baltimore, MD • On-site, Remote

Index Analytics
IT Services • 11 - 50 employees
Learn more about Index Analyticsexternal

$120K - $155K/yr

Other

Posted 14 days ago

Job description

Position Overview
Index Analytics is seeking a Senior Data Analyst / Researcher to support federal healthcare clients by applying advanced analytical, statistical, and research methodologies to transform complex healthcare and program data into actionable insights. This role combines deep expertise in Medicaid and CHIP programs, data analytics, policy research, and stakeholder engagement to support evidence-based decision-making, program oversight, and healthcare transformation initiatives.
The ideal candidate will be able to lead and conduct quantitative and qualitative analyses across diverse healthcare data sources, including claims, enrollment, utilization, quality, and performance datasets, to identify trends, evaluate program effectiveness, and inform policy and operational improvements. Working closely with data scientists, engineers, health policy researchers, and client stakeholders, this position develops high-impact analyses, predictive models, visualizations, and research products that support strategic priorities and improve outcomes for federal healthcare programs.
Key Responsibilities
  • Conduct research on best practices and analyze diverse Medicaid and Children's Health Insurance Program (CHIP) data sources including claims, TAF, program oversight metrics, scorecards, and performance data to identify patterns, clusters, and insights that inform policy, operational improvements, and feature development for CMS business needs.
  • Perform routine and exploratory data analysis using statistical methods, predictive modeling, and state-of-the-art data mining techniques to build predictive models, uncover trends, and generate actionable insights.
  • Develop and deliver high-quality reports, ad hoc analyses, and data visualizations grounded in HCD and UX best practices to help clients interpret complex information and support decision-making.
  • Work collaboratively in an agile environment with data analysts, data scientists, and internal/external clients to define analytical requirements, develop value-added solutions, and enhance business operations.
  • Prepare technical deliverables including presentation decks, reports, and draft manuscripts communicating findings to clients and scientific audiences through clear, compelling presentations that translate complex concepts for diverse stakeholders.
  • Integrate qualitative design research with exploratory data analysis to provide insights that support improved health policy and better outcomes for Medicaid and CHIP populations.
  • Use analytics and data expertise to provide input to and review of documentation, reference, and training materials related to analytic approaches, data structures and content, and data quality observations.

  • U.S. Citizen or otherwise authorized to work in the United States and able to demonstrate physical residency in the U.S. for at least three of the past five years. Must be eligible to support federal government clients and meet applicable background investigation requirements.
  • Bachelor's degree and a minimum of 10 years of professional experience, or an equivalent combination of education and experience. Four years of specialized experience may be substituted for a bachelor's degree. Candidates should possess significant experience supporting data analytics, business intelligence, research, or related healthcare and government initiatives.
  • Demonstrated experience applying statistical methods and analytical techniques, including probability distributions, hypothesis testing, regression analysis, predictive modeling, data mining, and advanced data visualization to support data-driven decision-making, program administration, policy development, and program oversight.
  • Strong experience conducting complex quantitative and qualitative analyses using large healthcare datasets to evaluate program performance, identify trends, measure outcomes, and support operational and policy improvements.
  • Hands-on experience performing data analytics using SQL, PySpark, or comparable technologies is required.
  • Direct experience working with Medicaid and CHIP data assets, including T-MSIS Analytic Files (TAF), CMS-416T reports, state performance measures, and health outcomes reporting, is required. Experience with DQ Atlas, Scorecard, or related CMS performance monitoring tools is preferred.
  • Experience developing reports, dashboards, data visualizations, and analytical products that effectively communicate findings to technical, business, and executive audiences.
  • Subject matter expertise in text analytics is preferred. Experience applying Natural Language Processing (NLP) techniques using Python or similar technologies is a plus.
  • Proficiency with Python, R, or other analytical programming languages is preferred.
  • Knowledge of healthcare policy, Medicaid and CHIP programs, population health, quality measurement, program evaluation, or healthcare performance improvement initiatives is highly desirable.
  • Experience supporting the Centers for Medicare & Medicaid Services (CMS) or other federal, state, or local government agencies is preferred.
  • Strong written, verbal, and presentation communication skills, with demonstrated ability to translate complex analytical findings into actionable recommendations for diverse stakeholder groups.
  • Proven ability to work collaboratively within cross-functional teams, manage multiple priorities, and support client-facing engagements in an Agile or fast-paced project environment.

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