Senior Data Engineer: Remote (PySpark & Databricks)

Truelogic; United States; Remote; USD 90,000 - 130,000; Full time

Benefits offered by this job

100% Remote Work

Competitive USD pay

Paid time off

Autonomy

Work with U.S. companies

Job summary

Truelogic is seeking a skilled Data Engineer with 5–7 years of hands-on experience to join our client’s data team. The ideal candidate will design, implement, and optimize ETL pipelines, perform data transformations, and push performance improvements using PySpark and Databricks.

You will collaborate with engineering and business stakeholders to translate requirements into robust data solutions, enforce data quality standards, and resolve pipeline bottlenecks, with a focus on cost efficiency in

Qualifications

• 5–7 years of hands-on data engineering experience.
• Strong PySpark and Databricks experience.
• Databricks AutoLoader and Python expertise.
• End-to-end ETL and data transformation knowledge.
• Ability to communicate across technical and business teams.
• Proven ability to optimize performance and compute costs.

Responsibilities

• Design, build, and maintain scalable ETL pipelines.
• Work with PySpark and Databricks to transform data.
• Tune pipelines for performance and cost-efficiency.
• Collaborate with engineers and business teams to translate requirements.
• Debug and resolve data bottlenecks.
• Enforce data quality and governance standards.

Skills

Problem-solving

Cross-functional communication

Team collaboration

Tools

PySpark

Databricks

Databricks AutoLoader

Python

Performance tuning

Job description

Truelogic is seeking a skilled Data Engineer with 5–7 years of hands-on experience to join our client’s data team. The ideal candidate will design, implement, and optimize ETL pipelines, perform data transformations, and push performance improvements using PySpark and Databricks.

You will collaborate with engineering and business stakeholders to translate requirements into robust data solutions, enforce data quality standards, and resolve pipeline bottlenecks, with a focus on cost efficiency in