Data Engineer I, SPIV Data Engineering

Selling Partner Identity Verification is working to identify and prevent fraudulent behavior through all stages of a seller's lifecycle. We are seeking a skilled Data Engineer to join our team and help us build and manage the Data for the business domains within the Selling Partner Identity Verification space. As a Data Engineer, you will play a crucial role in designing, implementing, and maintaining our data infrastructure and pipelines. You will work closely with cross-functional teams to deliver scalable and efficient data solutions that drive business value.

Key job responsibilities
You will design and develop scalable, high-performance data systems using medallion architecture.

You are expected to collaborate with data scientists, analysts, and business stakeholders to understand data requirements and implement appropriate solutions.

Optimize data storage and retrieval systems for improved performance and cost-effectiveness.

Implement data quality checks and monitoring systems to ensure data integrity and reliability.

A day in the life
A Data Engineer I on SPIV starts their day by syncing with the team on data pipeline status, checking for any overnight failures in ETL jobs processing seller identity verification data. They collaborate with data scientists to understand new fraud detection requirements, then design and implement scalable data models using medallion architecture to support these use cases.

About the team
SPIV Data Engineering builds the data backbone for Selling Partner Identity Verification - preventing fraud across the seller lifecycle. Our Northstar is to build business domain datasets with data as product mental model. We're a collaborative team that values ownership, technical rigor, and shipping solutions that matter. You'll work with data scientists, analysts, and business teams. We emphasize learning, code quality, and building infrastructure that scales.

Basic Qualifications

- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

Preferred Qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

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