On-site Data Platform Engineer: Scale Terabyte Pipelines

LG Ad Solutions; New York (NY); On-site; USD 80,000 - 110,000; Full time

Benefits offered by this job

Equal Opportunity Employment

Request for Accommodation

Job summary

LG Ad Solutions is seeking a highly driven Software Engineer to join our Data & Platform Engineering team. This on-site role in NYC focuses on designing and building robust, terabyte-scale data pipelines for advertising systems.

The ideal candidate will have a Bachelor's in Computer Science, 1-2 years of experience, and proficiency in Data Structures. Collaboration with teams in the US and India is essential, targeting core hours that overlap with US time zones.

Qualifications

• 1 to 2 years of relevant professional software development experience, strong preference for Backend or Data Engineering roles.
• Solid understanding of core Computer Science concepts, specifically Operating Systems, Computer Networks, and DBMS.
• Exceptional problem-solving and analytical skills, with proactive, self-starter attitude.

Responsibilities

• Build and maintain understanding across multiple complex data pipelines and platform projects owned by the team.
• Design, develop, test, and deploy robust backend services and data pipelines processing terabytes of data.
• Build and maintain internal tooling to enforce strict Data Governance and Data Compliance standards.

Skills

Data Structures and Algorithms

Problem-solving

Analytical skills

Technical communication

Education

Bachelor's Degree in Computer Science, Engineering, or related field

Tools

Apache Spark (PySpark or Scala-Spark)

Databricks

Apache Airflow

AWS, GCP, or Azure

Job description

LG Ad Solutions is seeking a highly driven Software Engineer to join our Data & Platform Engineering team. This on-site role in NYC focuses on designing and building robust, terabyte-scale data pipelines for advertising systems.

The ideal candidate will have a Bachelor's in Computer Science, 1-2 years of experience, and proficiency in Data Structures. Collaboration with teams in the US and India is essential, targeting core hours that overlap with US time zones.