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Airflow Dag Jobs (NOW HIRING)

Data Engineer

Jersey City, NJ · On-site

$119K - $143K/yr

Design, develop, and maintain complex Airflow DAGs for batch and event-driven data pipelines * Implement best practices for DAG performance, dependency management, retries, SLA monitoring, and ...

... Apache Airflow(DAG) • Good experience in Kubernetes, Terraform, Orchestration. Qualifications : Required : • 12+ years of experience • Hands on experience in Java/J2EE Programming • ...

Senior Data Platform Engineer

Austin, TX · On-site

$105K - $143K/yr

Experience with Airflow DAG development * Hands-on experience with GCP (Dataproc, BigQuery, Cloud Storage preferred) * Experience building APIs using FastAPI * Strong SQL and data modeling knowledge

Senior Data Platform Engineer

Phoenix, AZ · On-site

$100K - $135K/yr

Experience with Airflow DAG development * Hands-on experience with GCP (Dataproc, BigQuery, Cloud Storage preferred) * Experience building APIs using FastAPI * Strong SQL and data modeling knowledge

Senior Data Engineer

$108K - $147K/yr

Understanding of cloud-native solutions and architecture is critical • Strong knowledge and hands-on experience in as Python, SQL ,Terraform, CI/CD , Git flows, Attunity , AutoSys, Airflow Dag ...

Senior Data Platform Engineer

Phoenix, AZ · On-site

$105K - $143K/yr

Experience with Airflow DAG development * Hands‑on experience with GCP (Dataproc, BigQuery, Cloud Storage preferred) * Experience building APIs using FastAPI * Strong SQL and data modeling ...

Sr GCP Data Engineer

Austin, TX · On-site

$114K - $137K/yr

Experience with Airflow DAG development * Hands-on experience with GCP (Dataproc, BigQuery, Cloud Storage preferred) * Experience building APIs using FastAPI * Strong SQL and data modeling knowledge

Lead Data Engineer

Las Vegas, NV · On-site

$98K - $129K/yr

Apache Airflow (DAG development, workflow orchestration) * Snowflake * Advanced SQL (complex queries, performance tuning) * Apache Kafka (streaming, event-driven architectures) * API development and ...

DevOps Engineer:

Sunnyvale, CA · On-site

$61.50 - $84.25/hr

Resources such as EKS, Fargate, KMS, SQS, SNS, Lambda, IAM, s3, EFS, Elastic cache, Apache Managed Airflow , DAG , VPC, subnets, ALB, NLB, etc since we use all of these in our day-to-day activities ...

Data Analytics Engineer

Annapolis, MD · On-site

$113K - $136K/yr

Experience using Apache Airflow (DAG design, scheduling, operators, sensors) to orchestrate, schedule, and monitor complex workflows * Experience using Distributed Big Data processing engines ...

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Airflow Dag information

What is an Airflow DAG?

An Airflow DAG, or Directed Acyclic Graph, is a collection of tasks organized in a way that reflects their dependencies and execution order in Apache Airflow. It defines how and when tasks should run, ensuring a workflow's steps are executed in the correct sequence without cycles. DAGs are written in Python and are used to automate, schedule, and monitor complex data pipelines or workflows. By using Airflow DAGs, teams can manage and track their data processing jobs efficiently.

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

To excel as an Airflow DAG Developer, you need strong Python programming skills, experience with workflow orchestration, and a solid foundation in data engineering principles. Familiarity with Apache Airflow, version control systems like Git, and cloud platforms such as AWS or GCP is commonly required. Effective problem-solving, attention to detail, and clear communication are essential soft skills that set top performers apart. Mastery of these areas ensures efficient, reliable data pipeline development and smooth collaboration with data teams.

What are some common challenges faced when managing and maintaining Apache Airflow DAGs in a production environment?

One of the main challenges when working with Apache Airflow DAGs is ensuring reliable scheduling and execution, especially as the number and complexity of workflows grow. Issues like task dependencies, resource contention, and handling failures can arise, requiring careful design and monitoring. Additionally, managing code versioning and deployment of DAGs across development and production environments can be tricky, often necessitating robust CI/CD pipelines and clear collaboration between data engineers and DevOps teams. Regularly reviewing and optimizing DAG performance is also important to avoid bottlenecks and ensure scalability.

What is the difference between Airflow Dag vs Airflow Operator?

AspectAirflow DagAirflow Operator
DefinitionA collection of tasks organized to define a workflow in Apache Airflow.A single task or unit of work within a DAG, responsible for executing specific actions.
FunctionOrchestrates and schedules multiple tasks to run in a sequence or parallel.Performs a specific operation, such as data transfer or transformation.
UsageUsed to define entire workflows in Airflow.Used within DAGs to perform individual steps.
Credentials/CertificationsRequires knowledge of Python, Airflow setup, and scheduling concepts.Requires understanding of task-specific operations and Airflow operators.

In summary, an Airflow DAG is a blueprint for a workflow, organizing multiple tasks, while an Airflow Operator is a single task that performs a specific function within that workflow. Both are essential for building and managing data pipelines in Airflow.

Infographic showing various Airflow Dag job openings in the United States as of September 2026, with employment types broken down into 95% Full Time, 1% Part Time, and 4% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Java Tech Lead (Apache NiFi & Airflow) // Phoenix / Raleigh / Remote

Irving, TX • Remote

SmartIPlace
IT Services • 51 - 200 employees

$55 - $60/hr

Contractor

Re-posted 26 days ago


Job description

Job Description 

Must have hands-on Experience highlight:

  • 8–10 years of hands-on Java Tech Lead experience
  • Deep expertise in Apache NiFi (architecture, processors, controllers, clustering)
  • Strong experience in Apache Airflow (DAG design, schedulers, executors)
  • Hands-on data ingestion, transformation, routing, and enrichment pipelines
  • Experience building and integrating Java Spring Boot microservices
  • Strong exposure to event-driven and streaming architectures using Kafka

 

Must-Have Skills set (Technical & Functional):

  • Apache NiFi (data flows, cluster management, performance tuning)
  • Apache Airflow (workflow orchestration, DAGs, scheduling, optimization)
  • Java, Spring Boot, REST APIs, Microservices
  • Kafka (event-driven & streaming architecture)
  • SQL, Databases, Data transformation techniques
  • Data pipeline scalability, performance, and troubleshooting

 

Must have Keywords in resume:
Apache NiFi, Airflow, Java, Spring Boot, Microservices, Kafka, Data Pipelines, ETL, Workflow Orchestration, DAGs, Streaming Architecture, SQL

 

What exactly Client looking (Job description in short):
Client is seeking a Java Tech Lead with strong Apache NiFi and Airflow expertise to design, build, and optimize enterprise-scale data pipelines. The role focuses on data ingestion, transformation, routing, workflow orchestration, and performance tuning.


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About Smart-iPlace

Sourced by ZipRecruiter

SMART-iPLACE provides innovative staffing and consulting solutions that help our clients achieve their business objectives. We can understand and support all areas of your IT systems from back-end infrastructure to front-end personal productivity. Our goal is create innovative IT solutions that enable your business to be more agile and competitive.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Irving, TX, US

Year founded

2021

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