Data Engineer - Spark, Airflow & ETL Pipelines

TP-Link

Irvine (CA)

Vor Ort

USD 100.000 - 120.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Benefits dieser Stelle

Free snacks and drinks
Fully paid medical, dental, and vision
401K contribution
Bi-annual reviews and pay raises
Gym membership
Team-building events

Zusammenfassung

TP-Link Systems Inc. in Irvine, CA is seeking a data engineer to design and operate scalable ETL pipelines on AWS cloud.

You will build pipelines on EMR/Spark orchestrated by Airflow, write SQL and Python, ensure data quality, and partner with analysts to deliver reliable datasets for dashboards and analytics. Experience with dimensional modeling, star schema, and lakehouse formats recommended; comfortable with Parquet/ORC, S3, Git, and cost-conscious engineering.

Qualifikationen

  • 2–4 years of hands-on data development in production environments.

Aufgaben

  • Build, run, and own ETL pipelines on EMR (Spark) orchestrated in Airflow.
  • Write SQL and Python for data ingestion, transformation, and datasets for dashboards.
  • Own data quality checks and extend data models (fact & dimension, star schema).
  • Keep jobs efficient in runtime and cost, and automate repetitive tasks.
  • Collaborate with analysts and stakeholders to deliver usable datasets.

Kenntnisse

SQL
Python
Airflow
AWS
Data modeling
Git
Pandas
PySpark

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

Spark
Databricks
Airflow
Databases (SQL)
boto3

Jobbeschreibung

TP-Link Systems Inc. in Irvine, CA is seeking a data engineer to design and operate scalable ETL pipelines on AWS cloud.

You will build pipelines on EMR/Spark orchestrated by Airflow, write SQL and Python, ensure data quality, and partner with analysts to deliver reliable datasets for dashboards and analytics. Experience with dimensional modeling, star schema, and lakehouse formats recommended; comfortable with Parquet/ORC, S3, Git, and cost-conscious engineering.

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