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Data Engineer Airflow Jobs in Springfield, MA (NOW HIRING)

Develop data lake and warehouse solutions leveraging services like Snowflake for highperformance ... NumPy; AirFlow; Shell scripting; PL/SQL; Postgres SQL, and; GitHub. Required knowledge may be ...

Develop data lake and warehouse solutions leveraging services like Snowflake for highperformance ... NumPy; AirFlow; Shell scripting; PL/SQL; Postgres SQL, and; GitHub. Required knowledge may be ...

Develop data lake and warehouse solutions leveraging services like Snowflake for highperformance ... NumPy; AirFlow; Shell scripting; PL/SQL; Postgres SQL, and; GitHub. Required knowledge may be ...

Experience with an orchestration tool like Airflow * Experience with cloud technologies like ... College degree in Computer Science, Engineering, Statistics, Mathematics, Actuarial Science, Data ...

Experience with an orchestration tool like Airflow * Experience with cloud technologies like ... College degree in Computer Science, Engineering, Statistics, Mathematics, Actuarial Science, Data ...

Showing results 41-48

Data Engineer Airflow information

See Springfield, MA salary details

$44.3K

$129.3K

$176.9K

How much do data engineer airflow jobs pay per year?

As of Aug 19, 2026, the average yearly pay for data engineer airflow in Springfield, MA is $129,263.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,100.00 and $137,000.00 per year, depending on experience, location, and employer.

What does a data engineer specializing in Airflow do?

A Data Engineer specializing in Airflow is responsible for designing, building, and maintaining data pipelines using Apache Airflow, an open-source workflow orchestration tool. Their main job is to automate, schedule, and monitor complex data workflows, ensuring data moves reliably between systems and is processed efficiently. They often collaborate with data scientists, analysts, and other engineers to make sure that data is accessible, accurate, and up to date for business needs. Expertise in Airflow helps streamline data operations, optimize performance, and improve data pipeline reliability.

How does a data engineer specializing in Airflow typically collaborate with data scientists and analysts?

Data Engineers working with Airflow play a crucial role in enabling data scientists and analysts to access reliable, up-to-date data. They design and maintain ETL pipelines that automate data movement and transformation, ensuring data is clean and available for analysis. Collaboration often involves gathering requirements, troubleshooting pipeline issues, and optimizing data workflows to meet the needs of downstream users. Effective communication and documentation are essential, as data engineers must align technical solutions with the analytical goals of the broader team.

What are the key skills and qualifications needed to thrive as a data engineer specializing in Airflow, and why are they important?

To thrive as a Data Engineer with an Airflow focus, you need strong programming skills in Python, expertise in data pipeline design, and experience with distributed systems, often supported by a degree in computer science or a related field. Familiarity with Apache Airflow, cloud platforms (like AWS or GCP), and database technologies, as well as certifications in cloud data engineering, are typically required. Outstanding problem-solving, attention to detail, and effective communication help you collaborate on complex data workflows and troubleshoot issues efficiently. These skills ensure robust, scalable, and reliable data infrastructure, enabling organizations to make data-driven decisions with confidence.

What is the difference between Data Engineer Airflow vs Data Engineer?

AspectData Engineer AirflowData Engineer
Primary FocusWorkflow orchestration and pipeline automation using AirflowData collection, storage, transformation, and pipeline development
Required SkillsPython, Airflow, ETL processes, cloud platformsSQL, Python, ETL, data modeling, cloud services
Work EnvironmentData teams, cloud environments, automation pipelinesData warehouses, big data platforms, cloud infrastructure
CertificationsAirflow certifications, Python, cloud certificationsSQL, cloud certifications, data engineering certifications

While both roles involve data pipeline work, Data Engineer Airflow specializes in designing and managing workflows with Airflow, focusing on automation and orchestration. In contrast, Data Engineer has a broader scope, including data storage, transformation, and pipeline development across various tools and platforms.

What are popular job titles related to Data Engineer Airflow jobs in Springfield, MA?

For Data Engineer Airflow jobs in Springfield, MA, the most frequently searched job titles are:

What job categories do people searching Data Engineer Airflow jobs in Springfield, MA look for?

The top searched job categories for Data Engineer Airflow jobs in Springfield, MA are:

What cities near Springfield, MA are hiring for Data Engineer Airflow jobs?

Cities near Springfield, MA with the most Data Engineer Airflow job openings:

Infographic showing various Data Engineer Airflow job openings in Springfield, MA as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $129,263 per year, or $62.1 per hour.

Senior Lead Engineer

Quest Global

Windsor, CT • On-site

$140K/yr

Full-time

Medical, Retirement, PTO

Re-posted 13 days ago


Quest Global rating

7.8

Company rating: 7.8 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

243rd of 449 rated engineering


Job description

Job Requirements
Quest Global delivers world-class end-to-end engineering solutions by leveraging our deep industry knowledge and digital expertise. By bringing together technologies and industries, alongside the contributions of diverse individuals and their areas of expertise, we are able to solve problems better, faster. This multi-dimensional approach enables us to solve the most critical and large-scale challenges across the aerospace & defense, automotive, energy, hi-tech, healthcare, medical devices, rail and semiconductor industries.
We are looking for humble geniuses, who believe that engineering has the potential to make the impossible possible; innovators, who are not only inspired by technology and innovation, but also perpetually driven to design, develop, and test as a trusted partner for Fortune 500 customers. As a team of remarkably diverse engineers, we recognize that what we are really engineering is a brighter future for us all. If you want to contribute to meaningful work and be part of an organization that truly believes when you win, we all win, and when you fail, we all learn, then we're eager to hear from you.
The achievers and courageous challenge-crushers we seek, have the following characteristics and skills:
Position Responsibilities: Design and implement scalable, fault-tolerant data pipelines to process
both batch and streaming data. Develop data lake and warehouse solutions leveraging services like
Snowflake for highperformance analytical processing. Perform advanced data modeling and
optimization, including dimensional modeling, partitioning, clustering, and indexing to ensure
efficient data access. Enable realtime analytics and monitoring by building low-latency, event-
driven pipelines and integrating with visualization and alerting platforms. Implement data quality
checks, data lineage tracking, and metadata management to maintain trust and traceability across
the organization. Collaborate cross-functionally with product, analytics, and data science teams to
translate business use cases into scalable data solutions. Define and enforce data contracts and data
SLAs, ensuring accuracy, timeliness, and compliance across data products. Create reusable
frameworks for ETL orchestration, schema validation, and pipeline testing to accelerate
development across teams. Treating datasets as reusable products with versioning, contracts, and
SLAs. Own KPI definitions and metrics logic, ensuring consistent reporting and analytics across
business units. Partner with senior stakeholders to define strategic data goals, such as customer
360, churn prediction, or marketing attribution models. Measure and improve data adoption and
impact, including usage tracking, performance benchmarking, and cost optimization. Conduct
technical interviews and contribute to the development of internal best practices and coding
standards.
Position Requirements: Master's degree (or foreign equivalent) in Computer Engineering, or
related field, PLUS two (2) years of experience in the job offered or a related position. Experience
must include demonstrable knowledge of: Snowflake; DBT; Tableau; Python; Pandas; NumPy;
AirFlow; Shell scripting; PL/SQL; Postgres SQL, and; GitHub. Required knowledge may be
gained prior to or concurrently with Master's degree. Travel to unanticipated client locations
throughout the U.S., approximately 30% as required. Any suitable combination of education,
training, or experience is acceptable.
Apply at careers.quest-global.com and reference Job Code 28290.0303
Pay Range: USD $140,712/year .
Compensation decisions are made based on factors including experience, skills, education, and other job-related factors, in accordance with our internal pay structure. We also offer a comprehensive benefits package, including health insurance, paid time off, and retirement plan.

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