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Clinical Data Coding Jobs in Springfield, VA (NOW HIRING)

Axle

Data Engineer

Rockville, MD · On-site

$116K - $140K/yr

They are seeking a Data Engineer to support biomedical science, clinical research data integration ... Git, code review, automated testing, documentation, peer review, and change management. • ...

AArete

Senior Data Scientist

Vienna, VA · On-site

Leverage and mentor others in the use of AI assistive engineering tools such as Claude Code, GitHub ... clinical, claims, revenue cycle, population health, or healthcare operations data * AWS cloud ...

Digital Links Inc

Java Developer (HL7/FHIR)

Rockville, MD · On-site

$52 - $67.25/hr

... in clinical and public health data exchange contexts. * Optimize API performance, scalability, and reliability through code tuning, caching, and asynchronous processing. * Maintain clear ...

Digital Links Inc

Java Developer (Healthcare)

Rockville, MD · On-site

$52 - $67.25/hr

... in clinical and public health data exchange contexts. * Optimize API performance, scalability, and reliability through code tuning, caching, and asynchronous processing. * Maintain clear ...

rockITdata

Data Architect

Arlington, VA · Remote

$65.25 - $84/hr

Support low-code workflow automation solutions using Power Apps (as applicable). Required ... Experience moving clinical and/or claims data between disparate healthcare systems. * Experience ...

Ripple Effect

NLM Software Developer

Rockville, MD

$98K - $113K/yr

Job Code : CIT-DV-03/CIT-DB-03 * Location : Client/NIH Main Campus * Employee Type : Exempt ... Work closely with interdisciplinary teams of researchers, data scientists, clinicians, and IT ...

Ripple Effect

NLM Sr. Software Developer

Rockville, MD

$118K - $135K/yr

Job Code : CIT-DV-04T/CIT-DB-04T * Location : Client/NIH Main Campus * Employee Type : Exempt ... Work closely with interdisciplinary teams of researchers, data scientists, clinicians, and IT ...

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Clinical Data Coding information

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$59

$85

How much do clinical data coding jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for clinical data coding in Springfield, VA is $59.71, according to ZipRecruiter salary data. Most workers in this role earn between $47.21 and $71.06 per hour, depending on experience, location, and employer.

What does a clinical data coder do?

A clinical data coder reviews medical records and assigns standardized codes to diagnoses, procedures, and treatments using coding systems like ICD and CPT. This process ensures accurate billing, data analysis, and compliance with healthcare regulations, often requiring attention to detail and familiarity with coding software. Coders typically work in healthcare settings and may need certification such as CPC or CCS.

Will AI replace clinical coders?

AI can assist clinical data coders by automating routine coding tasks and improving accuracy, but it is unlikely to fully replace them. Human oversight remains essential for complex cases, quality assurance, and interpreting nuanced medical information. Clinical coders' expertise and understanding of medical terminology are critical in ensuring accurate and compliant coding practices.

What is a Clinical Data Coding job?

A Clinical Data Coding job involves assigning standardized medical codes to clinical data, such as diagnoses, procedures, and treatments, to ensure accurate documentation and facilitate healthcare analytics, billing, and research. Professionals in this role use coding systems like ICD, CPT, and SNOMED CT to classify medical information. They work with electronic health records (EHRs) and collaborate with healthcare providers, data analysts, and regulatory bodies. Accuracy and attention to detail are crucial, as coded data impacts patient care, compliance, and reimbursement.

What are the key skills and qualifications needed to thrive in the Clinical Data Coding position, and why are they important?

To thrive in Clinical Data Coding, strong knowledge of medical terminology, clinical research processes, and disease classification systems (such as ICD-10 or MedDRA) is generally required, often supported by a degree in life sciences or related fields. Familiarity with electronic data capture systems, clinical trial databases, and specialized coding software is essential, along with certifications like Certified Clinical Data Manager (CCDM) or Certified Clinical Research Professional (CCRP) being advantageous. Attention to detail, analytical thinking, and effective communication enhance quality and teamwork in this role. These skills and qualities ensure precise and compliant data coding, which is critical for research integrity, regulatory submissions, and high-quality clinical outcomes.

What does a typical day look like for someone working in Clinical Data Coding?

A typical day in Clinical Data Coding involves reviewing clinical trial data, assigning accurate codes to medical terms, adverse events, and procedures using standard classification systems, and ensuring compliance with regulatory standards. You’ll collaborate closely with clinical data managers, medical reviewers, and biostatisticians to resolve discrepancies and maintain data integrity. Additionally, you may attend team meetings to discuss coding conventions or project updates and perform quality checks on coded data. This role offers a structured environment where attention to detail and accuracy are highly valued, supporting the success of clinical research projects.

What pays more, CCS or CPC?

In the field of clinical data coding, Certified Coding Specialists (CCS) typically earn higher salaries than Certified Professional Coders (CPC) due to their advanced certification and specialized knowledge in hospital and inpatient coding. However, salaries can vary based on experience, location, and employer, with CCS roles often requiring more extensive training and credentials. Both certifications are valuable for career advancement in medical coding and billing.

How do I get into clinical coding?

To become a clinical data coder, typically you need a high school diploma or equivalent, followed by specialized training or certification in medical coding, such as the Certified Professional Coder (CPC) or Certified Coding Specialist (CCS). Gaining knowledge of medical terminology, anatomy, and coding systems like ICD-10 and CPT is essential, and some employers prefer candidates with experience in healthcare or related fields.
What are popular job titles related to Clinical Data Coding jobs in Springfield, VA? For Clinical Data Coding jobs in Springfield, VA, the most frequently searched job titles are:
What cities near Springfield, VA are hiring for Clinical Data Coding jobs? Cities near Springfield, VA with the most Clinical Data Coding job openings:
Clinical Data Coding jobs near you
Infographic showing various Clinical Data Coding job openings in Springfield, VA as of July 2026, with employment types broken down into 2% As Needed, 72% Full Time, 19% Part Time, and 7% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $124,203 per year, or $59.7 per hour.
Data Engineer

Data Engineer

Axle

Rockville, MD • On-site

$116K - $140K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Summary:
Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. They are seeking a Data Engineer to support biomedical science, clinical research data integration, and advanced data analysis initiatives by designing, building, optimizing, and maintaining data pipelines and workflows.
Responsibilities:
• Data Pipeline Development: Design, build, test, and maintain data pipelines to ingest, transform, harmonize, and integrate diverse biomedical and research data sources, including clinical, genomic, experimental, imaging, biospecimen, operational, and other scientific datasets. Develop reusable transformation logic and curated datasets that support analytics, reporting, dashboards, applications, APIs, and downstream research workflows.
• Data Integration and Lifecycle Support: Support the full research data lifecycle by enabling reliable data movement from source systems and storage environments into structured, analysis-ready formats. Assist with data ingestion, curation, metadata capture, data refreshes, source-to-target mapping, schema management, and long-term maintainability of data products and workflows.
• Collaboration: Work closely with data scientists, bioinformaticians, researchers, application developers, project managers, and government stakeholders to gather requirements and deliver practical data solutions. Translate scientific and operational data needs into technical specifications, data models, transformation logic, and reusable datasets that accelerate biomedical research workflows and support informed decision-making.
• Quality & Governance: Implement data validation checks, reconciliation routines, testing practices, and monitoring processes to ensure data accuracy, completeness, consistency, and integrity. Follow data governance and security best practices, including documentation of transformations, lineage, assumptions, access requirements, and compliance considerations related to sensitive, regulated, de-identified, or access-controlled research data.
• Dashboarding & Integration: Create or support interactive dashboards, reporting layers, APIs, and application-ready datasets that allow researchers and stakeholders to visualize, explore, and analyze data. Support integration between data pipelines, databases, cloud platforms, analytics environments, and approved application platforms to enable scalable and secure data access.
• Operational Support and Modernization: Troubleshoot data pipeline failures, source system inconsistencies, data quality issues, schema changes, access issues, and performance bottlenecks. Contribute to modernization efforts by improving automation, documentation, scalability, reproducibility, and platform readiness across environments.
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Data Science, Bioinformatics, Biomedical Informatics, Information Systems, Engineering, or a related field, or equivalent practical experience.
• Proven experience as a Data Engineer, Analytics Engineer, Data Integration Developer, Bioinformatics Engineer, or similar data-intensive role, preferably supporting analytics, biomedical research, healthcare, scientific computing, or research data teams.
• Strong proficiency in Python and SQL for data manipulation, transformation, scripting, automation, and analysis.
• Hands-on experience building ETL/ELT processes and data pipelines to support large, complex, multi-source datasets.
• Familiarity with scalable data processing approaches, including Spark/PySpark or similar frameworks, for high-volume or complex transformations.
• Solid understanding of data modeling, relational databases, data warehouses, data lakes, metadata, and database concepts.
• Ability to work with complex, multi-modal datasets, including structured, semi-structured, and unstructured data, and optimize data workflows for reliability, performance, usability, and long-term maintainability.
• Knowledge of software engineering and data engineering best practices, including version control using Git, code review, automated testing, documentation, peer review, and change management.
• Experience ensuring data quality and using lineage, provenance tracking, audit trails, or documentation practices to support transparency, reproducibility, and data flow traceability.
• Excellent problem-solving skills and the ability to communicate effectively with both technical and non-technical stakeholders.
• Comfortable working in an interdisciplinary environment with biomedical researchers, analysts, developers, and project teams.
• Capable of translating domain-specific needs into technical solutions and explaining technical risks, limitations, and dependencies in clear stakeholder-focused language.
• Strong interest in biomedical science, clinical research, healthcare data, and scientific discovery.
• Ability to quickly learn domain-specific concepts, data structures, terminology, and research workflows.
• Demonstrated awareness of sensitive data handling, privacy, access control, data governance, and regulatory or compliance expectations associated with biomedical and clinical research data.
Preferred:
• Hands-on experience building data solutions in modern data platforms or platform-as-a-service environments such as Snowflake, Databricks, Palantir, cloud data warehouses, data lakes, or similar platforms.
• Experience supporting integrations across databases, cloud storage, APIs, analytics platforms, dashboards, and application environments.
• Experience preparing curated datasets for dashboards, APIs, web applications, reporting tools, notebooks, or scientific computing environments.
• Familiarity with research-facing tools and platforms such as Posit Connect, R/Shiny, Streamlit, Jupyter, Galaxy, Code Ocean, or similar analytics and application delivery environments.
• Experience working with cloud or hybrid data environments, object storage such as S3, relational databases such as Postgres, automated data refreshes, scheduled jobs, API-based integrations, and secure data movement across controlled environments.
• Previous experience in biomedical research, healthcare analytics, clinical research, public health, pharmaceutical research and development, or scientific data management.
• Familiarity with biomedical data standards or datasets, such as clinical trial data, clinical imaging, laboratory data, biospecimen data, transcriptomics/genomic data, HL7/FHIR, CDISC, OMOP, or related standards.
• Experience supporting data governance, metadata management, data lineage, reproducible workflows, documentation standards, and secure handling of de-identified, sensitive, or access-controlled research datasets.
Company:
At Axle, we are driven by the mission to accelerate discovery and enhance organizational outcomes by revolutionizing operations with our innovative solutions. Founded in 2002, the company is headquartered in Rockville, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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