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Csv Validation Jobs in Michigan (NOW HIRING)

Capgemini

$46K - $111K/yr

Partner with QA teams to validate test cases and data accuracy Required Skills * Mandatory ... XML, fixed-length , and CSV formats * Healthcare standards: ICD-10, CPT, RxNorm , HCPCS * Six Sigma ...

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Acadia Pharmaceuticals Inc.

$165K - $207K/yr

Experience with clinical service providers and supporting functions such as eCOA, computerized system validation (CSV), and data management Physical Requirements While performing the duties of this ...

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Csv Validation information

What is a csv validation?

A CSV Validation job involves verifying the accuracy, consistency, and integrity of data within CSV files. This role ensures that the data conforms to predefined formats, standards, and business rules. It often includes checking for missing values, incorrect data types, and formatting errors to prevent issues in data processing. Professionals in this role may use scripts, validation tools, or manual reviews to ensure data quality and compliance.

What are the typical daily responsibilities of a csv validation professional?

CSV (Computer System Validation) Validation professionals are responsible for planning, executing, and documenting validation activities for computerized systems used in regulated industries like pharmaceuticals and biotechnology. Their daily tasks include reviewing system requirements, developing validation protocols, conducting risk assessments, performing validation testing, and documenting results in accordance with regulatory standards. They regularly collaborate with IT, quality assurance, and business stakeholders to ensure systems are compliant and audit-ready. Staying organized and detail-oriented is crucial, as each step must be carefully documented and verified for compliance. This role is dynamic and requires balancing technical tasks with frequent cross-functional communication.

What are the key skills and qualifications needed to thrive in the csv validation position, and why are they important?

To excel in CSV Validation, a strong background in life sciences, engineering, or a related technical discipline is essential, along with knowledge of computer system validation regulations and best practices. Familiarity with FDA 21 CFR Part 11, GAMP guidelines, and validation software tools is highly valued, and certifications such as PMP or CISA can be beneficial. Attention to detail, analytical thinking, and effective communication skills help professionals manage documentation and collaborate with cross-functional teams. These abilities ensure that systems in regulated environments are compliant, reliable, and meet industry standards for quality and data integrity.

What are popular job titles related to Csv Validation jobs in Michigan?

For Csv Validation jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Csv Validation jobs in Michigan look for?

The top searched job categories for Csv Validation jobs in Michigan are:

What cities in Michigan are hiring for Csv Validation jobs?

Cities in Michigan with the most Csv Validation job openings:

Csv Validation jobs near you
Infographic showing various Csv Validation job openings in Michigan as of August 2026, with employment types broken down into 83% Full Time, 13% Part Time, and 4% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

IT Data Engineer

Western Michigan University

Kalamazoo, MI • On-site

$65K - $76K/yr

Full-time

Medical, Retirement, PTO

Re-posted yesterday

Western Michigan University rating

6.7

Company rating: 6.7 out of 10

Based on 30 frontline employees who took The Breakroom Quiz

491st of 629 rated colleges and universities

Job description

Description
The Data Engineer is responsible for designing, building, and maintaining the organization's data
infrastructure to support a scalable, governed, and analytics-ready environment. This role focuses on the
development of robust data pipelines, integration of disparate data sources, and optimization of data storage
and processing frameworks, while contributing to the evolution of the WMed data platform toward a data-asa-
service (DaaS) model that enables standardized, secure, and reusable access to trusted data assets
across clinical operations, medical education, research, compliance, and executive decision-making.
Working within a modern data architecture, the Data Engineer transforms fragmented, system-centric data
into structured, reliable, and accessible datasets. This role partners closely with analysts, stakeholders, and
technical teams to ensure data availability, integrity, and performance across platforms including partner
EHRs, academic systems, and enterprise applications.
Please Note this is a Salaried Hybrid Position that will require on-site attendance. (MI residents only or willing to relocate - at their expense, to Michigan)
This position is NOT eligible for employer-sponsored work authorization (visa sponsorship), now or in the future.
BENEFITS
  • Wellness reimbursement.
  • Continuing education and tuition reimbursement.
  • Employer-funded retirement plan.
  • Two medical plan options: PPO and High Deductible Health Plan (HDHP) with employer HSA contribution.
  • Flexible work solutions based on position and department.
  • Up to four weeks of PTO accrual beginning in year one.
  • Paid holidays.
  • Paid volunteer time.
  • Paid preferred holiday.

DUTIES AND RESPONSIBILITIES:
  • Design, build, and maintain scalable data pipelines to ingest, transform, and load data from clinical, academic, and enterprise systems
  • Develop and manage ETL processes using tools such as Pentaho and Microsoft SSIS, ensuring reliability and performance
  • Design and implement data models and schemas to support downstream analytics and reporting use cases
  • Contribute to the development of a DaaS platform, enabling reusable, governed data products and standardized access patterns for analysts, applications, and self-service users
  • Optimize and maintain PostgreSQL data environments, including performance tuning and storage strategies
  • Implement and monitor data quality, validation, and error-handling processes
  • Establish and maintain data lineage, metadata, and documentation to support governance and transparency
  • Integrate new data sources into the data platform, including APIs, flat files, and third-party systems
  • Troubleshoot and resolve data issues across the full data lifecycle
  • Support the evolution of organizational data architecture toward a modern, scalable platform
  • Contribute to standards, best practices, and governance frameworks for data engineering
  • All other duties as assigned

Requirements
To perform this job successfully, an individual must be able to perform each duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
EDUCATION AND/OR EXPERIENCE:
  • Bachelor's degree in Computer Science, Information Systems, Engineering, or related technical field (or equivalent experience).
  • 3 to 5 years of experience in data engineering, ETL development, or data platform engineering.
  • Advanced proficiency in SQL (PostgreSQL or similar relational databases).
  • Experience designing and managing data pipelines and workflows (Pentaho, Microsoft SSIS, or similar ETL tools).
  • Experience working with healthcare data systems preferred.
  • Experience integrating academic and enterprise systems preferred.

OTHER SKILLS AND ABILITIES:
  • Strong understanding of data architecture, data modeling, and data warehousing concepts.
  • Experience building and maintaining data pipelines (ETL), orchestration, and scheduling frameworks.
  • Knowledge of data warehousing and/or data lake architectures.
  • Experience with data quality frameworks, validation, and monitoring.
  • Familiarity with performance tuning and query optimization.
  • Familiarity with modern data platform concepts including DaaS and self-service analytics enablement.
  • Understanding of data governance, lineage, and metadata management concepts.
  • Experience working with structured and semi-structured data (CSV, JSON, APIs).
  • Ability to troubleshoot data issues across multiple systems and layers (source to pipeline to warehouse to reporting).
  • Experience with BI tools (Power BI or similar) for downstream data consumption.
  • Ability to learn and adapt to evolving data technologies and platforms.

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