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Data Anonymization Jobs in Boston, MA (NOW HIRING)

ASCENSUS

Senior DevOps Engineer

Boston, MA · Hybrid

$141K - $181K/yr

Enforce data masking and PII anonymization for nonproduction environments * Validate data integrity and consistency postrefresh Observability, Reliability & Operations * Define and enforce ...

Ascensus, LLC.

Senior DevOps Engineer

Newton, MA · On-site

$142K - $183K/yr

Enforce data masking and PII anonymization for non-production environments * Validate data integrity and consistency post-refresh Observability, Reliability & Operations * Define and enforce ...

ASCENSUS

Senior DevOps Engineer

Newton, MA · Hybrid

$142K - $183K/yr

Enforce data masking and PII anonymization for nonproduction environments * Validate data integrity and consistency postrefresh Observability, Reliability & Operations * Define and enforce ...

Foundation Medicine

Director, Privacy Counsel

Boston, MA

$210K - $263K/yr

Advise FMI on matters related to informed consent, HIPAA authorization, research protocols, and secondary data use. * Advise FMI on de-identification, pseudonymization, and anonymization standards ...

All JobsData Anonymization JobsData Anonymization Jobs in Boston, MA

Data Anonymization information

See Boston, MA salary details

$50K

$179.3K

$264.5K

How much do data anonymization jobs pay per year?

As of Aug 2, 2026, the average yearly pay for data anonymization in Boston, MA is $179,276.00, according to ZipRecruiter salary data. Most workers in this role earn between $145,000.00 and $184,700.00 per year, depending on experience, location, and employer.

What is the highest paying data job?

The highest paying data jobs often include roles such as Data Science Director, Chief Data Officer, or Data Engineering Manager, with salaries exceeding $150,000 annually depending on experience and industry. These positions typically require advanced skills in data analysis, machine learning, and leadership, along with relevant certifications or advanced degrees. Compensation varies by company size, location, and individual expertise.

What is data anonymization?

Data anonymization is the process of transforming personal or sensitive data so that individuals cannot be identified, either directly or indirectly. This is typically achieved by removing or encrypting identifiers such as names, addresses, or social security numbers, and sometimes by aggregating data. The goal is to protect privacy while still allowing the data to be used for analysis or research. Data anonymization is crucial in complying with privacy regulations like GDPR and HIPAA. Properly anonymized data helps organizations minimize risk while making valuable data available for insights and decision-making.

How to become a data privacy specialist?

To become a data privacy specialist, individuals typically need a bachelor's degree in fields like computer science, information technology, or law, along with knowledge of data protection regulations such as GDPR or CCPA. Gaining certifications like Certified Information Privacy Professional (CIPP) or Certified Information Privacy Manager (CIPM) can enhance credibility, and experience with data management, security tools, and privacy policies is valuable in this role.

What is the difference between Data Anonymization vs Data Masking?

AspectData AnonymizationData Masking
PurposeTo permanently remove or alter identifiable information to protect privacyTo temporarily hide sensitive data for testing or training
MethodData is irreversibly transformedData is reversibly masked or obscured
Use CasesData sharing, privacy compliance, anonymized analyticsTesting, development, user training
Impact on DataData becomes non-identifiable and unusable for original purposesData remains usable but obscured

While both Data Anonymization and Data Masking aim to protect sensitive information, Data Anonymization permanently alters data to prevent re-identification, making it suitable for privacy compliance and sharing. Data Masking temporarily obscures data for testing or training, allowing data usability while protecting sensitive details.

Why has data anonymization not taken off?

Data anonymization as a job role has not gained widespread prominence because it is often part of broader data privacy and security roles rather than a standalone position. Challenges such as balancing data utility with privacy, technical complexity, and evolving regulations have limited its standalone demand, though skills in data masking, encryption, and compliance are valuable in related fields.

What are some common challenges faced by professionals working in data anonymization roles?

Professionals in data anonymization often encounter challenges such as balancing data utility with privacy, ensuring compliance with evolving data protection regulations, and addressing the risk of re-identification. The work typically involves collaborating closely with data engineers, analysts, and legal teams to determine the appropriate anonymization techniques for various datasets. Staying updated on new privacy tools and methodologies is crucial, as is adapting processes to fit the unique needs of each project or organization.

What does data anonymization do?

Data anonymization is a process used by data anonymization specialists to remove or obscure personally identifiable information from datasets, ensuring individual privacy while maintaining data utility. It helps organizations comply with data protection regulations and enables secure data sharing for analysis or research. Techniques include masking, pseudonymization, and generalization, often performed using specialized tools and requiring attention to data security standards.

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

To thrive in Data Anonymization, you need expertise in data privacy principles, knowledge of statistical methods, and a background in computer science, information security, or related fields. Familiarity with tools like ARX, sdcMicro, and programming languages such as Python or R, as well as understanding of regulations like GDPR, is typically required. Strong analytical thinking, attention to detail, and effective communication skills set professionals apart in this field. These competencies are crucial for ensuring sensitive information is protected while maintaining data utility for analysis and compliance.
What are popular job titles related to Data Anonymization jobs in Boston, MA? For Data Anonymization jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Data Anonymization jobs in Boston, MA look for? The top searched job categories for Data Anonymization jobs in Boston, MA are:
Data Anonymization jobs near you
Infographic showing various Data Anonymization job openings in Boston, MA as of July 2026, with employment types broken down into 6% Internship, 35% As Needed, 17% Full Time, 3% Part Time, 25% Temporary, and 14% Nights. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution, with an average salary of $179,276 per year, or $86.2 per hour.

Member of the Technical Team - Data Engineer

Transfyr

Cambridge, MA • On-site

$126K - $151K/yr

Full-time

Re-posted 23 days ago

Job description

Job Summary:
Transfyr is building physical AI for science, aiming to make real-world scientific work legible and reproducible. The Data Engineer role involves designing and building core systems for managing multimodal data from laboratory workflows, focusing on data pipelines, models, and quality control processes.
Responsibilities:
• Architect Scalable Pipelines: Lead the development of modern, observable data pipelines managing multimodal data transformations and scientific data using Prefect, Temporal, etc.
• Modernize Data Models: Develop and manage the data models used across our stack, ensuring they support business, operational, and scientific requirements.
• Streamline Scientific Data: Develop robust data contracts and versioning strategies to manage high-volume multimodal data streams from laboratory environments.
• Implement Automated Data Quality (QC): Establish robust, automated Quality Control (QC) processes and monitoring, ensuring the integrity, completeness, and correctness of multimodal data uploads (e.g., confirming video frames, checking for dropouts, and validating deployment association)
• Design for Privacy and Compliance: Architect data pipelines and storage solutions that address data anonymization, Personally Identifiable Information (PII) handling, and security requirements necessary for both customer data and internal compliance.
• Ship Tools People Trust: Develop internal and customer facing tools that make complex systems understandable and usable for scientists and operators.
• Enable What Comes Next: Evaluate and integrate external tools, open source software, and infrastructure components where they accelerate progress.
Qualifications:
Required:
• Deep experience with pipeline orchestration tools (Prefect/Temporal or similar preferred)
• Fluency in Python
• Infrastructure-as-code tooling
• Edge-to-cloud data transfers
• Proven track record of implementing data version control and data contracts to maintain high data quality
• Ability to build and scale data-intensive backend systems that handle multimodal scientific outputs like live video and sensor metadata
• Hands-on expertise with cloud data services (e.g., AWS S3, ECS, or similar infrastructure-as-code managed cloud compute/storage) for high-volume data ingestion and serving
• High agency
• Biased toward action
• Successful in ambiguity
• Thoughtful
• Clear, direct communicator
• Intense
Preferred:
• A passion for science
• A passion for and experience with AI
• Demonstrated experience working in fast-moving/ambiguous environments (like startups!)
Company:
Transfyr builds AI tools that help transfer scientific research into real-world applications and innovation. Founded in 2025, the company is headquartered in Boston, USA, with a team of 2-10 employees. The company is currently Early Stage.

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