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Airflow Developer Jobs in Wisconsin (NOW HIRING)

KOHLS

Senior Data Engineer

Menomonee Falls, WI · On-site

$106K - $144K/yr

About the Role As Senior Data Engineer, you will lead the development and ownership of domain data ... Experience using Airflow/Cloud Composer/Dagster for orchestration, transformation and CI/CD ...

Jedson Engineering Inc

Mechanical/HVAC Engineer

Appleton, WI · On-site

$80K - $108K/yr

... calculations Airflow and pressurization strategies Ductwork and piping layouts Equipment ... engineering, validation, quality assurance, automation and manufacturing teams Perform root cause ...

Acrisure

Software Engineer, Data

Stevens Point, WI · On-site

$111K - $133K/yr

Familiarity with orchestration tools such as Airflow,Dagster, orFivetran. * Experience working in cloud environments (GCP or Azure preferred). * Familiarity with DevOps processes and AIassisted ...

Pwc

Data Engineer - Senior Associate

Milwaukee, WI · On-site

$77K - $202K/yr

... Engineer - Senior Associate, you will focus on designing and building data infrastructure and ... Airflow and Apache Hadoop for scalable data processing and workflow management - Building and ...

Vertex Group

Data Engineer

Madison, WI · On-site

$115K - $138K/yr

Orchestration tools (Airflow, Dagster, or similar) * Healthcare / payer domain experience (Clinical, Claims, Provider) * Required Experience: * 7+ years of handson data engineering experience

Acrisure

Software Engineer, Data (L1)

Stevens Point, WI · On-site

$111K - $133K/yr

Familiarity with data orchestration technologies like Airflow, Dagster, or Fivetran * Experience with cloud environments; (GCP, Azure) a definite plus * Familiar with DevOps process * Experience ...

PS SEASONING & SPICES INC

Design Engineer

Hartford, WI · On-site

The Design Engineer is responsible for designing, developing, and improving smoker products and ... airflow, durability, and safety considerations into designs Manufacturing Support · Collaborate ...

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Airflow Developer information

What are Airflow Developers?

Airflow Developers are professionals who design, build, and maintain data workflows using Apache Airflow, an open-source platform for orchestrating complex computational workflows and data processing pipelines. They are responsible for writing, scheduling, and monitoring tasks (DAGs) that automate data movement and transformation across systems. Airflow Developers work closely with data engineers, analysts, and other stakeholders to ensure reliable and efficient data pipeline automation. Their expertise includes Python programming, Airflow configuration, troubleshooting, and best practices for scalable workflow management.

What is the difference between Airflow Developer vs Data Engineer?

AspectAirflow DeveloperData Engineer
Required CredentialsKnowledge of Apache Airflow, Python, SQLData modeling, SQL, Python, cloud platforms
Work EnvironmentFocus on workflow orchestration, automationData pipeline development, storage, processing
Industry UsageTech, finance, healthcare for workflow automationBroad industries for data infrastructure

While both roles involve working with data and Python, an Airflow Developer specializes in designing and maintaining workflow automation using Apache Airflow. In contrast, a Data Engineer builds and manages data pipelines and infrastructure across various tools and platforms. The roles often overlap but differ mainly in scope and focus.

What are the key skills and qualifications needed to thrive as an Airflow Developer, and why are they important?

To thrive as an Airflow Developer, you need strong programming skills in Python, experience with data pipelines, and a solid understanding of workflow orchestration concepts. Familiarity with Apache Airflow, cloud platforms (like AWS or GCP), and version control systems such as Git are typically required, along with knowledge of containerization tools like Docker. Analytical thinking, attention to detail, and effective communication are key soft skills for collaborating with data teams and troubleshooting complex workflows. These competencies ensure reliable, scalable, and maintainable data pipeline solutions that support organizational data needs.

What are some common challenges Airflow Developers face when managing complex data pipelines, and how can these be addressed?

Airflow Developers often encounter challenges such as managing dependencies between tasks, handling large-scale workflows, and ensuring reliable pipeline execution. To address these, it's essential to design modular DAGs (Directed Acyclic Graphs), implement robust error handling, and use features like sensors and retries strategically. Collaboration with data engineers and stakeholders is also key for troubleshooting and optimizing workflows. Effective monitoring and logging practices further help in quickly identifying and resolving issues.

Is Airflow in demand?

Airflow developers are in high demand due to the increasing need for data pipeline orchestration in organizations. Skills in Python, cloud platforms, and workflow management tools contribute to strong job prospects in this field.

What jobs in the US pay 300,000 a year?

For an Airflow Developer, earning $300,000 annually typically requires senior-level experience, specialized skills in data pipeline orchestration, and often working in large organizations or consulting roles. High salaries are common in roles involving complex data infrastructure, cloud platforms, and leadership responsibilities. Such compensation is less common for entry-level positions and usually involves additional bonuses or equity components.

What engineer makes $500,000 a year?

Senior engineers in specialized fields such as software engineering, data engineering, or cloud architecture can earn $500,000 or more annually, especially with extensive experience, advanced skills, and leadership roles. High compensation often includes bonuses, stock options, or profit sharing, particularly in large tech companies or startups with significant growth potential.

What jobs pay $500,000 a year in the US?

High-paying roles for Airflow Developers or similar data engineering professionals can reach or exceed $500,000 annually, especially with seniority, specialized skills, and experience in cloud platforms like AWS or GCP. Such compensation often includes base salary, bonuses, and stock options, typically found in senior or executive-level positions within large tech companies or financial institutions.
What are popular job titles related to Airflow Developer jobs in Wisconsin? For Airflow Developer jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Airflow Developer jobs in Wisconsin look for? The top searched job categories for Airflow Developer jobs in Wisconsin are:
What cities in Wisconsin are hiring for Airflow Developer jobs? Cities in Wisconsin with the most Airflow Developer job openings:
Airflow Developer jobs near you
Senior Data Engineer

Senior Data Engineer

KOHLS

Menomonee Falls, WI • On-site

$106K - $144K/yr

Other

Posted 13 days ago

Kohl's rating

5.7

Company rating: 5.7 out of 10

Based on 1,447 frontline employees who took The Breakroom Quiz

13th of 21 rated department stores

Job description

About the Role

As Senior Data Engineer, you will lead the development and ownership of domain data products, including batch, streaming and artificial intelligence/machine learning (AI/ML) feature pipelines. You will drive design decisions that improve data reliability, performance and governance maturity while standardizing patterns that scale across teams. You will partner cross-functionally to enable analytics, ML and GenAI use cases with trusted data.

What You’ll Do

  • Design, build and maintain batch, streaming and real-time Artificial Intelligence (AI)  feature pipelines to extract data from diverse source systems and producers (Application Programming Interfaces (APIs), events, databases, files) ensuring efficient ingestion, transformation and publishing

  • Design, refine and implement scalable data models, semantic layers and data contracts to promote consistency, reuse and accessibility

  • Owns the end-to-end data product lifecycle for the domain. Define and maintain data contracts, including service level agreements (SLAs), schema expectations, quality metrics and consumer ownership, to ensure a reliable and trustworthy experience

  • Partner with cross functional teams to co-design scalable data solutions that meet business needs and clearly define the boundaries between data pipeline responsibilities and model-building activities

  • Develop automated workflows and Continuous Integration / Continuous Deployment (CI/CD) pipelines using tools such as Airflow, Apache Spark and Python to drive reliability and faster delivery

  • Implement validation, observability and evaluation frameworks that ensure accuracy, lineage and timeliness across data pipelines and large language model (LLM) outputs

  • Apply and enforce governance, privacy and compliance standards (GDPR, PCI DSS, CCPA), ensuring data security and traceability

  • Partner with cross functional teams to translate business needs into technical data solutions that scale across domains

  • Drive performance tuning, automation and adoption of AI-powered data tools to enhance data platform efficiency

  • Mentor data engineers and champion best practices for maintainable, governed and reusable data assets

  • Own cost and performance tradeoffs for domain data products and monitor compute usage, storage growth and unit cost to implement optimizations that reduce spend while meeting SLAs

  • Additional tasks may be assigned

What Skills You Have

Required

  • 4+ years designing, building and optimizing data pipelines and models in production, ideally within large-scale cloud environments

  • Proficiency in SQL and Python (or Scala) for data development, testing and automation

Preferred

  • Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering or a related field

  • Experience with Apache Spark (or equivalent) for large-scale data processing and performance optimization

  • Experience using Airflow/Cloud Composer/Dagster for orchestration, transformation and CI/CD pipelines

  • Experience with cloud warehouses/lakes (BigQuery, Redshift, Snowflake) and object storage

  • Experience designing and optimizing streaming pipelines using Kafka, Pub/Sub, spark

  • Strong understanding of dimensional modeling, normalization and schema design for analytics and GenAI integration into data products

  • Experience with data testing, lineage, monitoring and observability frameworks to ensure data integrity and reliability

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