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Data Annotation Manager Jobs in Wisconsin (NOW HIRING)

Urus Group LP

Sr. Data Engineer

Madison, WI · On-site

$115K - $138K/yr

... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ... Advocate for best practices in managing sensitive biological and genetic data, including data ...

Farm Systems

Sr. Data Engineer

Madison, WI · On-site

$114K - $137K/yr

... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ... Advocate for best practices in managing sensitive biological and genetic data, including data ...

Urus

Sr. Data Engineer

Madison, WI · On-site

$114K - $137K/yr

... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ... Advocate for best practices in managing sensitive biological and genetic data, including data ...

Trans Ova Genetics

Sr. Data Engineer

Madison, WI · On-site

$115K - $138K/yr

... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ... Advocate for best practices in managing sensitive biological and genetic data, including data ...

AgSource

Sr. Data Engineer

Madison, WI · On-site

$114K - $137K/yr

... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ... Advocate for best practices in managing sensitive biological and genetic data, including data ...

Lorven Technologies

SAP SME Consultant - ABAP

Neenah, WI · On-site

$63.25 - $85.75/hr

The ideal candidate will be proficient in custom development, Z-table data extraction, and ... AMDP (ABAP Managed Database Procedures) * OData Services and Fiori integration * ABAP RESTful ...

Merck Group

CAD Project Specialist

Sheboygan Falls, WI · On-site

$80K - $122K/yr

Capable of building datasets and organizing data, leveraging AI tools and deploying them to search ... Basic understanding of CAD standards, layer management, block libraries, and annotation practices

All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in Wisconsin

Data Annotation Manager information

See Wisconsin salary details

$31.3K

$98.1K

$173.6K

How much do data annotation manager jobs pay per year?

As of Aug 5, 2026, the average yearly pay for data annotation manager in Wisconsin is $98,053.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,600.00 and $126,700.00 per year, depending on experience, location, and employer.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

What are the key skills and qualifications needed to thrive as a data annotation manager?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in Wisconsin? The most popular types of Data Annotation jobs in Wisconsin are:
What are popular job titles related to Data Annotation Manager jobs in Wisconsin? For Data Annotation Manager jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Data Annotation Manager jobs in Wisconsin look for? The top searched job categories for Data Annotation Manager jobs in Wisconsin are:
What cities in Wisconsin are hiring for Data Annotation Manager jobs? Cities in Wisconsin with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Wisconsin as of July 2026, with employment types broken down into 2% Locum Tenens, 33% Full Time, 26% Part Time, 2% Contract, 36% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution, with an average salary of $98,053 per year, or $47.1 per hour.

Sr. Data Engineer

Urus Group LP

Madison, WI • On-site

$115K - $138K/yr

Full-time

Re-posted 3 days ago

Job description

This role is responsible for the design, development, and maintenance of data integration, analytics, and reporting solutions that support our animal genetics and bioinformatics workloads. The ideal candidate will possess expertise in Databricks and modern data engineering tools such as Azure Data Factory, combined with hands on experience working with biological, genomic, or other omics datasets. This position requires a proactive, self-motivated, and results-oriented individual with a passion for data, a strong understanding of data architecture and warehousing principles, and an appreciation for bioinformatics workflows in a commercial genetics environment. 

Responsibilities 

Data Integration 

  • Design, develop, and maintain robust and efficient ETL/ELT pipelines and processes on Databricks for both operational and bioinformatics datasets (e.g., genomic markers, phenotypic data, laboratory outputs). 

  • Ingest, transform, and harmonize structured and semi-structured biological data from lab systems, LIMS, sequencing platforms, and external partners into the enterprise data platform. 

  • Troubleshoot and resolve Databricks pipeline errors and performance issues. 

  • Optimize data flow performance and minimize data latency across scientific and business use cases. 

  • Implement data quality checks, validations, and reconciliation processes within ETL workflows, including domain-specific checks for genomic and phenotypic data. 

Databricks Development 

  • Develop and maintain Databricks pipelines, notebooks, and datasets using Python, Spark, and SQL. 

  • Optimize Databricks jobs for performance and cost-effectiveness, including largescale bioinformatics and analytics workloads. 

  • Integrate Databricks with other data sources and systems, including lab instruments, genomic databases, and on-prem or cloud data stores. 

  • Participate in the design and implementation of data lake architectures that support both traditional analytics and bioinformatics pipelines. 

Data Warehousing 

  • Participate in the design and implementation of data warehousing solutions to support reporting, analytics, and scientific modeling. 

  • Model and curate subject areas for genetics, reproduction, and bioinformatics (e.g., animals, pedigrees, genotypes, breeding values, trials). 

  • Support data quality initiatives and implement data cleansing procedures across business and scientific domains. 

Reporting and Analytics 

  • Collaborate with business users, scientists, geneticists, and bioinformaticians to understand data requirements for department-driven reporting and analytics needs. 

  • Maintain and extend the existing library of complex dashboards and visualizations to surface genetic, reproductive, and operational insights. 

  • Enable self-service analytics for R&D and product teams by exposing well- governed, documented data products. 

  • Troubleshoot and resolve report issues, including performance bottlenecks and data inconsistencies. 

Cloud Platform Experience 

  • Apply strong programming skills in Python, SQL, and Spark to build scalable data and bioinformatics workflows. 

  • Use CI/CD and IaC tools (Terraform, ARM, CloudFormation) to automate deployment of data platform components and analytics environments. 

  • Design and implement Databricks platform architecture on Azure and AWS infrastructure, including environments that support largescale scientific computation. 

  • Contribute to cloud security, governance, and cost optimization practices for data and bioinformatics workloads. 

Bioinformatics and Scientific Collaboration 

  • Partner with geneticists, biostatisticians, and bioinformaticians to translate scientific requirements into scalable data and platform architectures. 

  • Support or orchestrate bioinformatics pipelines (e.g., variant processing, quality control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks capabilities. 

  • Ensure that data models, pipelines, and storage structures meet the needs of downstream analytics, predictive models, and genetic evaluations. 

  • Advocate for best practices in managing sensitive biological and genetic data, including data governance, access control, and compliance with relevant standards and regulations. 

Collaboration and Communication 

  • Thrive in an entrepreneurial, self-starting, and fast-paced environment, working both independently and with our highly skilled teams. 

  • Collaborate effectively with business users, data analysts, scientists, and other IT teams. 

  • Communicate technical information clearly and concisely, both verbally and in writing, to technical and nontechnical stakeholders. 

  • Document all development work, data models, and procedures thoroughly, including bioinformatics and scientific data flows. 

Continuous Growth 

  • Keep abreast of the latest advancements in data integration, cloud platforms, bioinformatics tooling, and data engineering technologies. 

  • Continuously improve skills and knowledge through training and self-learning in both data engineering and bioinformatics domains. 

Requirements 

  • Bachelor's degree in Computer Science, Information Systems, Bioinformatics, Computational Biology, or a related field; a Master's degree is an asset. 

  • 7+ years of experience in data integration and reporting, with experience designing and operating cloud-based data platforms. 

  • Extensive experience with Databricks, including Python, Spark, and Delta Lake. 

  • Strong proficiency with relational databases (e.g., SQL Server, RDS), including TSQL, stored procedures, and functions. 

  • Experience with data warehousing concepts and best practices. 

  • Experience with Microsoft Azure cloud platform; exposure to Microsoft Fabric is desirable. 

  • Hands on experience working with biological, genomic, or other omics datasets in a bioinformatics or life sciences setting (e.g., sequence data, SNP arrays, GWAS outputs, phenotypic traits). 

  • Familiarity with common bioinformatics tools, data formats (e.g., FASTQ, VCF, PLINK), and workflows is highly desirable. 

  • Strong analytical and problem-solving skills, with the ability to reason about complex data and scientific requirements. 

  • Excellent communication and interpersonal skills. 

  • Ability to work independently and as part of a cross-functional team across IT, science, and business. 

  • Experience with Agile methodologies. 

  • Demonstrated background in bioinformatics or computational biology, preferably supporting genetics, breeding, or life science research in an applied or commercial context. 

  • Must be legally authorized to work in the United States.

As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry - Alta Genetics, GENEX, Genetics Australia, Leachman Cattle, Jetstream, PEAK, SCCL, Trans Ova Genetics and VAS.  Each organization has its unique identity, products, and services. These companies work globally to provide cutting-edge dairy and beef genetics, customized reproductive services to maximize conceptions, dairy management information to take producers to the frontline of progressive dairy farming, and an array of products and services to help bovines reach their full genetic potential. URUS has 9 brands in 17 retail countries and employs nearly 2,800 people globally.


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