Senior Big Data Engineer - Data Warehouse & ETL

Lambdanets Services; Columbus (OH); On-site; USD 120,000 - 160,000; Full time

Job summary

Lambdanets Services is seeking a Big Data Developer to contribute at a senior level to data warehouse design and data preparation in Columbus, OH. You will transform data, optimize warehouse structures for reporting, and establish robust patterns for engineers and non-technical users.

Applicants should have 5+ years in Big Data, hands-on Hadoop stack experience, and a track record managing multi-petabyte environments. Collaboration across teams is essential to maintain an scalable Big Data stack.

Qualifications

• Bachelor’s degree in a computer-related field or equivalent.
• 5+ years of relevant Big Data experience.
• Experience with Data Warehouse design and ETL.
• Hands-on experience with Hadoop stack (MapReduce, HDFS, Pig, Hive).
• Experience in multi-petabyte data environments and distributed systems.

Responsibilities

• Contribute at a senior-level to data warehouse design and data preparation.
• Perform data transformations to populate warehouse tables optimized for reporting.
• Establish design patterns for engineers and non-technical users.
• Design and document components for data extraction and analysis on Big Data platforms.
• Ensure best practices in Big Data stack and mentor team members.
• Collaborate with multiple groups to maintain Big Data stack operability.

Skills

Data Warehouse design

ETL

Hadoop Stack

Distributed systems

Team collaboration

Education

Bachelor's degree in CS/IT

Tools

HDFS

Pig

Hive

Job description

Lambdanets Services is seeking a Big Data Developer to contribute at a senior level to data warehouse design and data preparation in Columbus, OH. You will transform data, optimize warehouse structures for reporting, and establish robust patterns for engineers and non-technical users.

Applicants should have 5+ years in Big Data, hands-on Hadoop stack experience, and a track record managing multi-petabyte environments. Collaboration across teams is essential to maintain an scalable Big Data stack.