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Data Engineer Airflow Jobs in Draper, UT (NOW HIRING)

... a Staff Data Warehouse Engineer and help build the trusted data backbone that powers decision ... Deep knowledge of ETL/ELT design, ingestion methods, and orchestration (e.g., dbt, Airflow or ...

We'd love to chat if you have: * 8+ years of experience in data engineering or combined software ... Deep knowledge of ETL/ELT design, ingestion methods, and orchestration (e.g., dbt, Airflow or ...

... Airflow, Debezium, Iceberg, Trino, and Spark. * Mentor international engineering teams ... Data Fanaticism: A relentless focus on using data to improve system performance, cost-efficiency ...

... Airflow, Debezium, Iceberg, Trino, and Spark. * Mentor international engineering teams ... Data Fanaticism: A relentless focus on using data to improve system performance, cost-efficiency ...

This individual will help drive data governance, data quality, and scalable analytics solutions ... Experience with Airflow or other orchestration platforms. * Experience working with modern AI tools ...

Showing results 21-40

Data Engineer Airflow information

See Draper, UT salary details

$41.6K

$121.3K

$165.9K

How much do data engineer airflow jobs pay per year?

As of Aug 7, 2026, the average yearly pay for data engineer airflow in Draper, UT is $121,265.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $128,500.00 per year, depending on experience, location, and employer.

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 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.

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.

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 popular job titles related to Data Engineer Airflow jobs in Draper, UT? For Data Engineer Airflow jobs in Draper, UT, the most frequently searched job titles are:
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What cities near Draper, UT are hiring for Data Engineer Airflow jobs? Cities near Draper, UT with the most Data Engineer Airflow job openings:

Senior Software Engineer- Big Data & MCP, Data Foundations

RevSpring

Salt Lake City, UT โ€ข On-site

$110K - $133K/yr

Full-time

Re-posted 9 days ago


Job description

Job Summary:
RevSpring is a company focused on providing innovative data solutions, and they are seeking a Senior Software Engineer specializing in Big Data and data foundations. The role involves designing and optimizing data pipelines, developing backend services, and ensuring data performance and quality in a healthcare context.
Responsibilities:
โ€ข Collaborate and Innovate: Partner with product managers, data engineers, and business leaders to translate complex product and data requirements into scalable, reliable data pipelines and the search experiences they power.
โ€ข Architect Data Pipelines: Design, build, and optimize large-scale distributed batch and streaming pipelines (using Apache Airflow, Apache Beam/Dataflow, and DBTon BigQuery) to ingest, model, and transform high-volume healthcare data into clean, well-tested, query-ready datasets and search indices.
โ€ข Build Data Models & Backend Services: Develop resilient Python services and DBT models that power data delivery and self-service analytics, including Model Context Protocol (MCP) servers that expose curated data and tooling to downstream and AI consumers, and integrate with external REST/SOAP APIs and third-party data sources.
โ€ข Optimize Data & Search Performance: Deeply tune pipeline throughput, data warehouse performance, and search indexing โ€” optimizing BigQuery cost and query performance and Elasticsearch index design to ensure data freshness, relevance, and scalability across high-volume datasets.
โ€ข Drive Engineering Excellence: Write clean, maintainable, well-tested code and lead by example through rigorous code reviews, architectural and data-modeling design discussions, and mentoring, driving a culture of high-quality software and trustworthy data.
โ€ข Pioneer New Technologies: Stay at the forefront of modern data engineering, the analytics-engineering ecosystem (e.g., DBT, BigQuery), and information retrieval, proactively applying these advancements to strengthen our data platform and the products it powers.
Qualifications:
Required:
โ€ข Proven experience designing and orchestrating large-scale ETL/ELT pipelines using Apache Beam/Google Cloud Dataflow (or similar), and DBT, built on modern cloud data warehouses.
โ€ข 4+ years of experience working with relational databases and analytical data warehouses, with deep, advanced SQL skills and solid data-modeling fundamentals (e.g., dimensional and normalized modeling).
โ€ข Working experience with search indexing and Elasticsearch, including index management, mappings, and building and maintaining search indices from pipeline output.
โ€ข Experience building scalable Python services and high-performance data APIs, including developing Model Context Protocol (MCP) servers that expose data and tooling to downstream and AI consumers.
โ€ข Strong understanding of containerization (Docker), CI/CD methodologies (e.g., GitHub Actions), Git, Infrastructure as Code (e.g., Terraform/Pulumi), and managing services within cloud platforms.
โ€ข Familiarity with healthcare data standards (e.g., NPPES/NPI registries, NUCC Provider Taxonomy, machine-readable files (MRFs) for cost transparency, and FHIR).
โ€ข Experience with data quality and pipeline testing frameworks (e.g., dbt tests, Great Expectations) and streaming/event ingestion (e.g., Pub/Sub, Kafka).
โ€ข Experience integrating graph-based data and healthcare taxonomy ontologies to enrich datasets and search query context.
โ€ข Experience with observability and logging platforms (e.g., DataDog) for monitoring pipeline health and data freshness.
โ€ข Bachelorโ€™s Degree
โ€ข 5+ years of professional experience with Python, with strong software-engineering fundamentals (testing, code review, design).
โ€ข 3+ years experience with Java or another JVM language is also high desired, particularly for Beam/Dataflow.
โ€ข Ability to read, analyze and interpret general business periodicals, professional journals, technical procedures or governmental regulations.
โ€ข Ability to write reports, business correspondence and procedure manuals.
โ€ข Ability to effectively present information and respond to questions from a variety of both internal and external sources.
Preferred:
โ€ข BigQuery experience is a plus.
โ€ข Familiarity with hybrid (BM25 + semantic/vector) search is a plus.
โ€ข 3+ years of GCP experience preferred.
Company:
RevSpring is a provider of revenue cycle technology services offering data analytics, multi-channel customer communications. Founded in 1997, the company is headquartered in Wixom, USA, with a team of 501-1000 employees. The company is currently Late Stage.