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Strong employer brand, metro location, and a popular mid-level data engineering role.
Core data engineering skills transfer across industries though finance domain knowledge adds some restriction.
Multiple mandatory technologies and an explicit Airflow experience requirement enforce strict filtering.
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Own and manage end-to-end Apache Airflow orchestration including DAG development, deployment on Kubernetes, and SLA alerting.
Build and maintain scalable, fault-tolerant ETL/ELT pipelines and implement lakehouse architecture using Apache Iceberg and Databricks.
Collaborate with data scientists and ML engineers to deliver trusted, governed data products and maintain platform tooling and engineering standards.
At least 3 years of production experience running Apache Airflow including scheduler internals and advanced features.
Strong Python skills applied to DAG development with test coverage and maintainable code.
Hands-on experience deploying and operating Airflow on Kubernetes using KubernetesExecutor or CeleryKubernetesExecutor.
Work Experience Required: Not explicitly mentioned in the JD beyond Airflow experience.
Experienced data engineer with deep expertise in scalable data pipeline orchestration, lakehouse architectures, and production-grade Airflow.
Comfortable working closely with ML and data science teams to operationalize feature pipelines and data products.
Practitioner capable of contributing reusable platform components and driving engineering standards across a collaborative team.