Familiarity with data anonymization or pseudonymization techniques * Experience supporting ML/AI data preparation workflows * DevOps experience (CI/CD pipelines, containerization) Deliverables ...
Familiarity with data anonymization or pseudonymization techniques * Experience supporting ML/AI data preparation workflows * DevOps experience (CI/CD pipelines, containerization) Deliverables ...
Data Engineer - Manager
$99K - $232K/yr
... anonymization and security best practices in complex systems - Excelling in dimensional modeling and data pipeline management - Leading teams in data warehouse troubleshooting and performance tuning ...
Data Engineer - Manager
$99K - $232K/yr
... anonymization and security best practices in complex systems - Excelling in dimensional modeling and data pipeline management - Leading teams in data warehouse troubleshooting and performance tuning ...
Full Stack Developer (.Net)
Saint Paul, MN · On-site
$75 - $80/hr
Familiarity with data anonymization or pseudonymization techniques * Experience supporting ML/AI data preparation workflows * DevOps experience (CI/CD pipelines, containerization) Deliverables ...
Quick apply
Full Stack Developer (.Net)
Saint Paul, MN · On-site
$75 - $80/hr
Familiarity with data anonymization or pseudonymization techniques * Experience supporting ML/AI data preparation workflows * DevOps experience (CI/CD pipelines, containerization) Deliverables ...
... with data anonymization or pseudonymization techniques Experience supporting ML/AI data preparation workflows DevOps experience (CI/CD pipelines, containerization) Deliverables and Expectations ...
... with data anonymization or pseudonymization techniques Experience supporting ML/AI data preparation workflows DevOps experience (CI/CD pipelines, containerization) Deliverables and Expectations ...
Senior Data Engineer, Infrastructure & Compliance
Minneapolis, MN · Remote
$110K - $150K/yr
Develop and implement automation for data governance, quality checks, and compliant data masking/anonymization processes, helping to secure data consumption by internal and external teams.
Senior Data Engineer, Infrastructure & Compliance
Minneapolis, MN · Remote
$110K - $150K/yr
Develop and implement automation for data governance, quality checks, and compliant data masking/anonymization processes, helping to secure data consumption by internal and external teams.
Senior Data Engineer, Infrastructure & Compliance
Minneapolis, MN · On-site
$110K - $150K/yr
Develop and implement automation for data governance, quality checks, and compliant data masking/anonymization processes, helping to secure data consumption by internal and external teams.
Senior Data Engineer, Infrastructure & Compliance
Minneapolis, MN · On-site
$110K - $150K/yr
Develop and implement automation for data governance, quality checks, and compliant data masking/anonymization processes, helping to secure data consumption by internal and external teams.
Data Anonymization information
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.
Why is anonymization a challenge of cyber security?
Data anonymization is a challenge in cybersecurity because it involves balancing data privacy with the need for data utility, making it difficult to remove all identifying information without losing valuable insights. Skilled professionals must understand techniques like masking and encryption, and stay updated on evolving threats and standards to effectively protect sensitive data while maintaining compliance.
What is the difference between Data Anonymization vs Data Masking?
| Aspect | Data Anonymization | Data Masking |
|---|---|---|
| Purpose | To permanently remove or alter identifiable information to protect privacy | To temporarily hide sensitive data for testing or training |
| Method | Data is irreversibly transformed | Data is reversibly masked or obscured |
| Use Cases | Data sharing, privacy compliance, anonymized analytics | Testing, development, user training |
| Impact on Data | Data becomes non-identifiable and unusable for original purposes | Data 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.
How to make a career in data privacy?
A career in data privacy often involves roles like data privacy analyst or data protection officer, requiring knowledge of data protection laws such as GDPR and CCPA. Building skills in data security, privacy frameworks, and tools like data masking and encryption, along with relevant certifications like CIPP or CIPM, can enhance job prospects in this field.
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 a data privacy specialist do?
A data privacy specialist is responsible for developing and implementing policies to protect sensitive information and ensure compliance with data privacy laws. They analyze data handling processes, conduct risk assessments, and often work with tools like data masking and encryption to safeguard personal data. Their role helps organizations prevent data breaches and maintain user trust.
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 privacy regulations and reduces the risk of data breaches during analysis or sharing. Skills in data masking, encryption, and understanding of privacy standards are important for this role.
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 Minnesota? For Data Anonymization jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Data Anonymization jobs in Minnesota look for? The top searched job categories for Data Anonymization jobs in Minnesota are:
What cities in Minnesota are hiring for Data Anonymization jobs? Cities in Minnesota with the most Data Anonymization job openings:
Job description
We are seeking a Full‑Stack Developer with strong experience in backend development using C#/.NET and frontend development using Angular, along with solid data engineering skills. This role will support the development and enhancement of a data platform used for ingesting, managing, and analyzing large‑scale imaging and electrophysiology procedural datasets.
The ideal candidate is hands‑on across the full stack and comfortable working with data pipelines, APIs, and scalable enterprise systems.
Key Responsibilities
- Design and develop full‑stack web applications for data ingestion, management, visualization, and labeling
- Build and maintain backend services and REST APIs to support data workflows
- Design and implement data pipelines for ingesting, transforming, and storing large datasets
- Integrate data from multiple external systems and sources
- Support longitudinal data tracking and versioning across datasets
- Collaborate closely with data scientists, R&D engineers, and domain experts
- Ensure performance, reliability, and maintainability of the platform
- Follow best practices for data security, privacy, and compliance
Required Skills & Qualifications
Full‑Stack Development
- Strong experience in full‑stack development (frontend and backend)
- Strong experience with C# and .NET (.NET Core / .NET Framework)
- Frontend: Strong experience with Angular (TypeScript, HTML, CSS)
- Experience with RESTful APIs and microservices architecture
Data Engineering
- Experience designing and building data pipelines (ETL/ELT)
- Strong data modeling skills
- Experience with PostgreSQL and NoSQL databases
- Familiarity with cloud‑based data storage and compute (Azure preferred)
Preferred / Nice‑to‑Have Skills
- Experience working with large imaging and time‑series datasets
- Exposure to healthcare, clinical, or regulated data environments
- Familiarity with data anonymization or pseudonymization techniques
- Experience supporting ML/AI data preparation workflows
- DevOps experience (CI/CD pipelines, containerization)
Deliverables & Expectations
- Scalable and reliable data ingestion and management capabilities
- Well‑documented, production‑quality code
- Improved support for longitudinal and multi‑source data workflows
- Close collaboration with internal teams to meet project timelines
About Sharpedge Solutions
Sourced by ZipRecruiter
Sharpedge Solutions is a leading IT business staffing and product development company providing quality cost-effective staffing services and cutting-edge solutions to various industries across the spectrum. Our tailored approach is driven by industry insight, and seasoned professionals ensure our clients stay ahead of the curve in the fast-evolving technology market. With over 20 years of successful operations and exceptional growth history, we take great pride in providing cost-effective quality services that improve performance, eliminate the backlog, and reduce IT costs while increasing the return organizations get from their investments and assets.
Industry
It services
Company size
51 - 200 Employees
Headquarters location
Westmont, IL, US
Year founded
2000