Data Engineer , GTMC Science

Key Skills

Java Programming, Design, Management, Python Programming, SQL, AWS, Machine learning techniques, Data science techniques, R Programming, Data Engineering, Data pipelines, ETL frameworks

Job Description

Do you love data? Do you want to work with AWS technologies for computing, storing, and analyzing data? Are you interested in being part of a data engineering team that supports an interdisciplinary science team to develop data science and analytical products to facilitate Amazon’s growth and development of talent across all of our businesses and locations around the world.

Join, Global Talent Management and Compensation (GTMC) team that is centrally responsible for managing and evolving Amazon’s human capital through intelligent talent products and processes.

Our vision in GTMC Science is to use data, machine learning and science to scalably solve organizational challenges focused on talent movement, talent differentiation, employee-role matching, promotion processes, organizational design and succession planning, diversity and inclusion, and new areas that address the evolving needs of our diverse employee base.

We are looking for an experienced, self-driven, analytical, and strategic Data Engineer. In this role, you will be working in a large and complex data warehouse environment. You should be passionate about working with disparate datasets and be someone who loves to bring data together to answer business questions. You should have deep expertise in the creation and management of datasets and the proven ability to translate data into meaningful insights through collaboration with Analysts, Researchers, Data Scientists, Applied Scientists and Software Engineers.

Our Data Engineers build and maintain the GTMC data lake infrastructure and data pipelines for various applications using Amazon best practices in software engineering, data management, data storage, data compute, and distributed systems. We are passionate about solving business problems with data.

Key job responsibilities

In this role, you will have the opportunity to display and develop your skills in the following areas:

• Develop and support data ETL pipelines with robust monitoring and alarming capabilities leveraging Native AWS technologies.
• Develop data models that are optimized for business and usability.
• Develop and optimize data tables using best practices for partitioning, compression, parallelization, etc.
• Develop and maintain metadata, data catalog, documentation, and data governance for internal business customers.
• Help internal business customers troubleshoot and optimize SQL and ETL solutions to solve reporting and analytics problems.
• Work with internal business customers and software development teams to gather and document requirements for data publishing and consumption.
• Proficient in Python, Java, R or some combination

  Knowledge of data management fundamentals and data storage principles

• Knowledge of distributed systems as it pertains to data storage and computing

BASIC QUALIFICATIONS

• Bachelor's degree in Computer Science, Info Systems, Business, or related field
• 2+ years of Data Engineering experience including one or more query languages (e.g. SQL), schema definition languages (e.g. DDL), ETL tools, and scripting languages (e.g. Python) to build data solutions
• Track record delivering large scale data solutions (e.g., data model, data architecture, data flow design, tool) in difficult or ambiguous data areas (new or existing systems)

PREFERRED QUALIFICATIONS

• Builds data solutions that are easy for others to contribute to. Knows how to make data auditable, available, and accessible.
• Has significant knowledge/experience with data design approaches and industry technologies.
• Is able to evaluate end-to-end data designs for strengths and weaknesses (data quality, scalability, latency, security, performance, data integrity, etc.).
• Can anticipate data access patterns and remove bottlenecks. Asks the right questions and drives the right technical solution(s).
• Is able to split development work into parallel tasks that can be performed by them and others and reassembled successfully.
• Understands that not all problems are new (or require new data solutions). Is able to make appropriate architectural trade-offs (e.g. build or buy a Business Intelligence technology? Tiered storage strategy?). Shows good judgment when making technical trade-offs between short-term technology needs and long-term business needs
• Has proactively fixed an architecture deficiency. Resolves root cause.
• Is able to drive data engineering best practices (e.g. Data Discovery, Naming Conventions, Operational Excellence, and Data Security) and set standards.
• Understands system limitations, scaling factors, boundary conditions, and/or the reasons for architectural decisions