Data Engineer, Stores TA

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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Overview
Amazon.com strives to be Earth's most customer-centric company where people can
find and discover virtually anything they want to buy online. By giving customers
more of what they want - low prices, vast selection, and convenience - Amazon.com
continues to grow and evolve as a world-class e-commerce platform. Amazon's
evolution from Web site to e-commerce partner to development platform is driven by
the spirit of innovation that is part of the company's DNA. The world's brightest
technology minds come to Amazon.com to research and develop technology that
improves the lives of shoppers and sellers around the world.
Job Description
Are you passionate about designing and building robust data pipelines that power
data-driven insights for business impact? Do you thrive in an environment where you
can leverage the latest data engineering technologies to tackle complex challenges?
We are seeking a talented Data Engineer to join our Talent Acquisition (TA) team and
help drive our data-powered analytical revolution.
In this role, you will be responsible for designing and developing highly efficient data
pipelines that seamlessly extract, transform, and load data from a diverse array of
sources. You will work closely with our Business Intelligence, Data Science, and
Product teams to uncover new data sources, optimize data delivery, and deliver high-
quality, actionable insights to support our TA initiatives.
Responsibilities:
Design and develop highly efficient data pipelines that seamlessly extract,
transform, and load data from a diverse array of sources using SQL, Python, and
AWS big data technologies.
Oversee and continuously enhance production operations, optimizing data
delivery, redesigning infrastructure for greater scalability, managing code
deployments, addressing bugs, and coordinating overall release management.
Establish and uphold best practices for the design, development, and support of
data integration solutions, including comprehensive documentation.
Collaborate closely with Product teams, Data Scientists, Software Developers,
and Business Intelligence Engineers to uncover new data sources and deliver
high-impact insights.
Demonstrate proficiency in reading, writing, and debugging data processing and
orchestration code in Python/Scala, adhering to the highest coding standards
(e.g., version control, code review, etc.).
Basic qualifications:
Bachelor's degree in Computer Science, Data Science, Engineering, or a related
technical field
Minimum 3-5 years of experience in data engineering or a similar role
Hands-on experience designing, developing, and maintaining data pipelines and
data integration solutions
Proficient in SQL, Python, and AWS big data technologies (e.g., EMR, Glue,
Athena, Redshift)
Strong understanding of data architecture, data modeling, and ETL/ELT
processes
Experience building/operating highly available, distributed systems of data
extraction, ingestion, and processing of large data sets
Experience with version control systems (e.g., Git) and code review best practices
Exposure to building scalable and fault-tolerant data processing systems
Preferred Qualifications:
Advanced experience with cloud-native data engineering tools and platforms
(e.g., Databricks, Snowflake, Kafka, Kinesis)
Proficiency in writing high-performance, scalable, and maintainable code (e.g.,
using design patterns, unit testing, refactoring)
Familiarity with data streaming and real-time data processing frameworks
Exposure to machine learning and artificial intelligence techniques for data-
driven insights
Proven track record of leading data engineering projects from inception to
delivery
Experience in mentoring and training junior data engineers
Demonstrated ability to identify and implement process improvements, optimize
data pipelines, and enhance overall data infrastructure
Passion for staying up-to-date with the latest data engineering trends and
technologies, and a willingness to explore new tools and approaches