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# Data Pipeline Engineer - Apache Airflow (Astronomer) & Python

- **Company:** TRIADE LLC
- **Location:** United States (Remote available)
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Information Engineering, Extract Transform Load (ETL), File Transfer, Python (Programming Language), SQL Databases, Data Logging, GitHub Copilot, AI Coding Agents, Pyspark, Apache Kafka, Virtual Agents, GPT, Data Pipelines
- **Published:** October 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=41823bf35722426c

## About the Role

* Strong Python and PySpark for data engineering - clean, tested, reviewed code, written without AI assistance.
 * You have deployed to production yourself, through CI/CD, in an environment with change control.
 * SQL and comfort with warehouse/lakehouse patterns.
 * Comfort working under strict security controls - no AI tools, no copy and paste, no file transfer, monitored sessions.
 * English at a working professional level. You will write for and speak to a financial services client. This one is not negotiable.
 * Available to start in early November and to work United States business hours. There is no extended ramp on this engagement.

## Description

* No AI-assisted development. AI coding assistants and generative AI tools (GitHub Copilot, ChatGPT or similar) may not be used for any work on this engagement. You write the code yourself.
 * No copy and paste into or out of the environment.
 * No file upload or download - nothing leaves or enters the environment except through the client's approved channels.
 * Activity is monitored. Sessions and actions in the environment are logged and reviewed.

A breach of these rules can end the engagement immediately, damage your professional reputation and Triade's, and expose the people involved to legal action. If you cannot work this way, this role is not for you.

What you will do

 * Create ETL/ELT pipelines in Python and PySpark - ingestion, transformation, orchestration, retries, idempotency, backfills.
 * Author and operate Apache Airflow DAGs - dependencies, sensors, dynamic task mapping, SLAs, alerting. Keep a daily chain running when one task fails.
 * Deploy to production - CI/CD, code review, environment promotion, release discipline in a controlled, audited environment.
 * Migrate and implement at volume - a large backlog of jobs to stand up; repeatability and patterns matter more than bespoke cleverness.
 * Own operational quality - monitoring, logging, cost and runtime, incident response on your own pipelines.
 * Communicate in writing, in English - pull requests, runbooks, status to the client., * Expert-level Apache Airflow in production. Not coursework, not "exposure" - you have owned DAGs that other people depended on., * Financial services or another regulated environment - ideally inside a bank's controlled environment.
 * dbt, Kafka; AWS, Azure or GCP.
 * Experience on a large migration rather than greenfield only.

How we work

 * Fully remote, United States business hours.
 * Written status over verbal - a decision that is not written down did not happen.
 * You work alongside data architects and platform engineers, and with partner delivery teams.