> Markdown version of [/jobs/ext/3814562-airflow-architect-big-data-systems](https://www.wearedevelopers.com/jobs/ext/3814562-airflow-architect-big-data-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Airflow Architect Big Data Systems - **Company:** LIVEMINDZ SERVICES INC - **Location:** Austin, TX, United States - **Experience:** Experienced - **Contract:** Contract - **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Amazon S3, Big Data, Cloud Database, Cloud Engineering, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, DevOps, Apache Hadoop, Python (Programming Language), Meta-Data Management, NoSQL, Performance Tuning, Data Mesh, DataOps, Shell Script, SQL Databases, Real Time Systems, Apache Spark, Microsoft Fabric, Data Lakes, Pyspark, Kubernetes, Deployment Automation, Apache Kafka, Data Pipelines, Docker, Databricks - **Published:** October 18, 2026 - **Apply:** https://www.dice.com/job-detail/10be286e-b10e-476e-bcaa-ab67bf6dafe8 ## About the Role Airflow & Data Engineering Expert knowledge of Apache Airflow DAG design, scheduling, operators, sensors, and executors. Strong experience in workflow orchestration, metadata management, and pipeline monitoring. Hands-on expertise in ETL/ELT development and data pipeline automation. Big Data Technologies Apache Spark / PySpark Hadoop ecosystem Apache Kafka Data Warehousing concepts Data Lakes and Lakehouse architectures NoSQL databases Programming Python (advanced) SQL (advanced) Shell scripting Core Java (preferred) Cloud Platforms AWS (EMR, Glue, S3, Redshift) Preferred Qualifications 10+ years of experience in data engineering and big data platforms. 3+ years of architecture experience designing enterprise data solutions. Experience with Kubernetes, Docker, and cloud-native deployments. Knowledge of Data Mesh, Data Fabric, and Modern Data Platforms. Experience leading architecture reviews and technology strategy discussions. Success Metrics Pipeline reliability and SLA adherence Workflow performance optimization Reduced operational incidents Improved data quality and observability Scalable and reusable orchestration frameworks ## Description Need an experienced Airflow Architect to design, architect, and lead enterprise-scale data orchestration solutions for modern big data platforms. The ideal candidate will have deep expertise in Apache Airflow, Big Data technologies, cloud-native architectures, and data engineering best practices to build scalable, reliable, and high-performance data pipelines., Architect and implement large-scale data orchestration frameworks using Apache Airflow. Design, develop, and optimize ETL/ELT pipelines for batch and near real-time processing. Build scalable workflows integrating Hadoop, Spark, Kafka, Databricks, data lakes, and data warehouses. Define Airflow deployment strategies, DAG standards, security, monitoring, and governance. Implement workflow scheduling, dependency management, error handling, retries, alerting, and SLA monitoring. Lead cloud-based data platform implementations on AWS Optimize pipeline performance, scalability, reliability, and cost efficiency. Establish CI/CD processes and DevOps practices for Airflow environments. Collaborate with business stakeholders, data engineers, architects, and platform teams to translate requirements into technical solutions. Mentor engineering teams on Airflow architecture, data orchestration patterns, and best practices.