ETL & Spark Data Engineer — AWS + Airflow

TP-Link Systems Inc.; United States; On-site; USD 100,000 - 120,000; Full time

Benefits offered by this job

Fully paid medical, dental, and vision

401k contributions

Four weeks PTO

Bi-annual pay raises

Wellness benefits

Team-building events

Job summary

TP-Link Systems Inc. is seeking a data engineer to build, run, and own ETL pipelines on EMR (Spark) orchestrated with Airflow. You’ll write SQL and Python for data ingestion and create datasets that analysts rely on, while ensuring data quality and scalable models.

You’ll work with AWS services (S3/Parquet), practice dimensional modeling, and optimize jobs for performance and cost in a production environment.

Qualifications

• 2–4 years of hands-on data development in a production environment.
• Strong SQL with window functions and multi-table joins.
• Python for production ETL (PySpark, pandas, boto3).
• Spark on a cloud platform (AWS EMR, Databricks, Glue).
• Experience with a scheduler (Airflow preferred).
• Dimensional modeling: fact and dimension tables, star schema.

Responsibilities

• Build, run, and own ETL pipelines on EMR (Spark) using Airflow.
• Write SQL and Python for data ingestion and dataset construction.
• Own data quality checks before shipping data products.
• Develop data models with fact and dimension tables.
• Optimize jobs for runtime and cost; automate repetitive tasks.
• Extend patterns and templates; automate what you repeat.
• Collaborate with analysts to turn requests into usable datasets.

Skills

SQL proficiency

Python ETL

Spark on cloud

Airflow

Dimensional modeling

AWS / S3 / Parquet

Linux / Git

AI-assisted data problem solving

Education

Bachelor's degree in CS/IS or related field

Tools

AWS EMR

Databricks/Glue

Airflow

S3/Parquet/ORC

Job description

TP-Link Systems Inc. is seeking a data engineer to build, run, and own ETL pipelines on EMR (Spark) orchestrated with Airflow. You’ll write SQL and Python for data ingestion and create datasets that analysts rely on, while ensuring data quality and scalable models.

You’ll work with AWS services (S3/Parquet), practice dimensional modeling, and optimize jobs for performance and cost in a production environment.