Senior Data Platform Engineer

Eitacies Inc

Phoenix (AZ)

On-site

USD 110,000 - 130,000

Full time

14 days+
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Job summary

A data engineering firm based in Phoenix, AZ, is seeking a Senior Data Platform Engineer. This role involves designing and maintaining Airflow DAGs for data workflows, optimizing Spark jobs, and developing APIs using FastAPI. The ideal candidate should have over 6 years of experience in data engineering, proficient Python skills, and familiarity with GCP. An excellent opportunity for professionals looking to enhance data quality and operational efficiencies in a collaborative environment.

Qualifications

  • 6+ years of data engineering experience.
  • Strong Python programming skills.
  • Production experience with Apache Spark and PySpark.
  • Hands-on experience with GCP preferred.

Responsibilities

  • Design and maintain Airflow DAGs for production data workflows.
  • Develop and optimize Spark / PySpark jobs on Dataproc.
  • Monitor pipeline performance, retries, and SLA compliance.

Skills

Python programming
Apache Spark / PySpark
Airflow DAG development
SQL transformation
Data modeling
API development using FastAPI

Tools

GCP (Dataproc, BigQuery, Cloud Storage)

Job description

Overview

Senior Data Platform Engineer — Onsite Phoenix, AZ

Responsibilities
  • Design and maintain Airflow DAGs for production data workflows
  • Develop and optimize Spark / PySpark jobs on Dataproc
  • Build and optimize complex SQL transformations
  • Develop REST APIs using FastAPI Python to support data services
  • Implement robust data quality and validation frameworks
  • Monitor pipeline performance, retries, and SLA compliance
Qualifications
  • 6+ years of data engineering experience
  • Strong Python programming skills
  • Production experience with Apache Spark PySpark
  • 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
  • Experience implementing data quality checks (schema validation, row count checks, anomaly detection)
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