Come and be part of the International Seller Services (ISS) Central Analytics Data Engineering (DE) team and work on solving problems!
We are a team of DEs who support Applied Scientists, Data Scientists, and Economists in experimenting, researching, and transforming machine learning, deep learning, and AI research into innovative products for our customers.
ISS is seeking a smart, highly motivated, and experienced Data Engineer to join our team. In this role, you'll help create the right Data and ML infrastructure.
As a Data Engineer, you will provide technical leadership, lead data engineering initiatives, and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective.
You should have a passion for working with large datasets and experience in organizing and curating data for analytics. A strategic and long-term vision for architecting advanced data ecosystems is essential.
You should have experience in building efficient, scalable data services and integrating data systems with AWS tools and services to support various customer use cases and applications.
Key job responsibilities
1. Provide technical leadership, lead data engineering initiatives, and build scalable, reliable analytical solutions.
2. Work with large datasets, focusing on data organization and curation for analytics.
3. Design, implement, and operate large-scale, high-performance data structures for analytics and data science.
4. Implement data ingestion routines using best practices in data modeling and ETL/ELT processes leveraging AWS technologies and big data tools.
5. Translate business and functional requirements into scalable, robust data solutions with flexible architectures.
6. Collaborate with engineering teams to adopt best practices in data system creation, data integrity, testing, analysis, validation, and documentation.
7. Identify opportunities for improvements in existing data solutions.
BASIC QUALIFICATIONS
1. 1+ years of data engineering experience.
2. Experience with data modeling, warehousing, and building ETL pipelines.
3. Experience with query languages such as SQL, PL/SQL, HiveQL, SparkSQL, or Scala.
4. Experience with scripting languages like Python or KornShell.
5. Knowledge of writing and optimizing SQL queries for large-scale, complex datasets.
PREFERRED QUALIFICATIONS
1. Experience with big data technologies such as Hadoop, Hive, Spark, EMR.
2. Experience with ETL tools like Informatica, ODI, SSIS, BODI, or DataStage.
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