Job Description

Overview

JAGGAER is looking for a Data Engineer to join our Data Pipeline team and help build the technologies used to drive our advanced Analytics capabilities. You will be part of a team responsible for the vision and architecture of scalable data pipeline solutions utilizing various data movement and transformation technologies. You will join a strong team of data engineers what will create high-performance, optimized, and robust date pipeline processes to move data from disparate sources from around the globe.

Principal Responsibilities

Contribute, as part of a team, in designing, testing, and implementing sophisticated data pipeline technologies to extract and transform data from source systems
Perform data-modeling on target systems to store data and support analytic querying
Communicate with other development teams to gather and document requirements
Work in Agile methodology and participate in design and review meetings
Follow best practices for the software development life-cycle including coding standards, reviews, source management, build and testing
Collaborate with other engineers in the team to implement best-practices around large scale data processing

Position Requirements

3+ years of experience as a Data Engineer or in a similar role working with large data sets and ELT/ETL processes
4+ years of industry experience in software development
Knowledge and practical use of a wide variety of RDBMS technologies such as MySQL, Postgres, SQL Server or Oracle
Use of cloud-based data warehouse technologies including Snowflake.
Strong SQL experience with an emphasis on analytic queries and performance
Experience with various “NoSQL” technologies such as MongoDB or ElasticSearch
Familiarity with either native database or external change-data-capture technologies
Practical use of various data formats such as CSV, XML, JSON, and Parquet
Use of Data flow and transformation tools such as Apache Nifi or Talend or PDI.
Implementation of ELT processes in languages such as Java, Python or NodeJS
Use of large, shared data stores such as Amazon S3 or Hadoop File System

Thorough and practical use of various Data Warehouse data schemas (Snowflake, Star)