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Airflow Developer Jobs in New York (NOW HIRING)

Python Developer

Rahway, NJ · On-site

$51 - $70.25/hr

Expert-level proficiency with Databricks, DBT and Apache Airflow. * Programming: Mastery of Python and solid database/SQL expertise. * AWS Cloud Depth: Hands-on experience with core AWS services: EC2 ...

Python Developer [Onsite]

Jersey City, NJ · On-site

$52.50 - $72.25/hr

Python Developer Location: Jersey City, NJ Requirement: We're seeking an experienced Python ... Design and implement Airflow DAGs to manage complex interdependent ETL workflows. * Migrate ...

The Role This is an engineering role sitting at the intersection of Man Group's Discretionary ... Kubernetes, Airflow * AI tooling: Claude Code, LLM agents, vector search, RAG - we actively build ...

DevOps Engineer

Manhattan, NY · Remote

$54 - $74/hr

... Title: DevOps Engineer Location: Manhattan, NY (Onsite / Offsite) Duration: 12+ Months with ... Experience in AWS Services Airflow EC2 AWS networking concepts VPC Subnets, NAT, S3, IAM , KMS, EMR ...

Scala Developer

New York, NY · On-site

$58 - $75.25/hr

Scala Developer Location: NYC, NY (Looking for only locals or near by who can do in person ... Experience with Apache Spark, Kafka, Databricks, and Airflow These roles provide an excellent ...

Data Engineer

Jersey City, NJ · On-site

$119K - $143K/yr

Must have: -Python -Apache Airflow/DBT -Communication, both written & verbal -Kubernetes -OpenShift -8+ years of experience We are seeking a highly skilled Senior Data Engineer with 8+ years of hands ...

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Airflow Developer information

What is an Airflow developer?

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.
What are popular job titles related to Airflow Developer jobs in New York? For Airflow Developer jobs in New York, the most frequently searched job titles are:
What job categories do people searching Airflow Developer jobs in New York look for? The top searched job categories for Airflow Developer jobs in New York are:
What cities in New York are hiring for Airflow Developer jobs? Cities in New York with the most Airflow Developer job openings:
Infographic showing various Airflow Developer job openings in New York as of August 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 92% In-person, 4% Hybrid, and 4% Remote job distribution.

Apache Airflow Engineer - Hybrid

Acunor Infotech

Manhattan, NY • On-site

$126K - $151K/yr

Other

Posted 11 days ago


Job description

Role - Apache Airflow Engineer

Location - New York, NY (3 Days to Office)

Mode Hybrid

Skills- Cybermation to Apache Airflow migration, Airflow DAG development, shell script/API scheduling, operational validation, runbook creation, and production support

Job Summary

We are seeking Apache Airflow migration resources to support a client initiative focused on migrating existing Cybermation-scheduled scripts to Apache Airflow. The resources will analyze the current Cybermation schedules, understand the existing shell/API invocation patterns, convert the schedules into Airflow DAGs, validate execution behavior, and support production deployment and handover.

The migration objective is to preserve the existing business execution behavior while improving schedule control, monitoring, logging, retry handling, operational visibility, and supportability through Apache Airflow.

Key Responsibilities

  • Analyze existing Cybermation job schedules, dependency patterns, runtime parameters, execution frequency, owners, success/failure behavior, and operational support expectations.
  • Review existing shell scripts and API/cURL invocation logic to determine the appropriate Airflow DAG design, task structure, error handling, and retry configuration.
  • Design, develop, and maintain Apache Airflow DAGs to orchestrate shell scripts, API calls, and dependent jobs with appropriate scheduling and operational controls.
  • Configure Airflow schedules, task dependencies, retries, logging, alerting hooks, variables, connections, parameters, and environment-specific configuration as per client standards.
  • Perform dry runs, unit testing, schedule validation, API response validation, failure/retry validation, and production-readiness checks for migrated jobs.
  • Create migration mapping, validation evidence, deployment notes, operational runbooks, and support handover documentation.
  • Coordinate with client SMEs, application owners, infrastructure teams, and release teams to support deployment, issue triage, initial run monitoring, and hypercare.

Preferred Qualifications

  • Prior experience migrating jobs from Cybermation or equivalent enterprise schedulers to Apache Airflow.
  • Experience deploying or operating Airflow in cloud or containerized environments is preferred.
  • Exposure to AWS services such as EKS, S3, CloudWatch, Lambda, Step Functions, IAM, Secrets Manager, or EventBridge is a plus.
  • Experience supporting financial services, capital markets, or other regulated enterprise environments is preferred.
  • Familiarity with ITSM processes, release governance, change management, and production support procedures.

Education & Experience

  • Bachelor's degree or equivalent experience in Computer Science, Information Technology, Engineering, or a related discipline.
  • 7+ years of overall engineering experience with hands-on Airflow, Python, and Linux/shell exposure.
  • Strong practical understanding of job scheduling, batch/workflow orchestration, production readiness, and operational support processes.

Role Deliverables

  • Migration inventory and mapping of Cybermation schedules to target Airflow DAGs.
  • Developed and configured Airflow DAGs for in-scope shell script and API/cURL-based workflows.
  • Validated execution evidence covering dry runs, schedule validation, API response checks, failure/retry validation, and defect remediation.
  • Deployment notes, operational runbooks, manual re-run steps, monitoring instructions, and production support handover documentation.
  • Post-deployment validation and hypercare support for migrated workflows.

Tools & Technologies

Apache Airflow, Python, Linux/Unix, Shell Scripting, cURL, REST APIs, Git, CI/CD basics, YAML/JSON configuration, scheduler migration, job monitoring, logging, retry handling, production support, runbook documentation, and optional AWS services such as EKS, S3, CloudWatch, Lambda, IAM, Secrets Manager, EventBridge, and Step Functions.


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About Acunor

Sourced by ZipRecruiter

Acunor provides high quality digital engineers in the field of Java Full Stack Programming, Pega, Appian, Power BI, Salesforce, DevOps, No-Code & Low-Code, Data Science, Analytics, Data Base and Cloud Native solutions. ​We specialize in providing Java Full Stack Engineers, BPM (Pega, Appian) Consultants, Salesforce Consultants, AWS/Azure/GCP Engineers, Data Scientists, Technical PMs, Program and Engagement Managers. ​Management comprises of highly experienced and seasoned technology executives with vast expertise in Large Scale Development Projects, Cloud Native Solutions and Managed Services.

Industry

It services

Company size

11 - 50 Employees

Headquarters location

Princeton, NJ, US

Year founded

2016

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