Data Engineer, Speed Products, Science and Analytics

4 weeks ago

Seattle WA United States Amazon Full time
Data Engineer, Speed Products, Science and Analytics

As a Data Engineer, you will be working in one of the world's largest and most complex data warehouse environments. You will design, implement and support scalable data infrastructure solutions to integrate with multi heterogeneous data sources, aggregate and retrieve data in a fast and safe mode, curate data that can be used in reporting, analysis, machine learning models and ad-hoc data requests. You will be managing multiple Redshift clusters that support reporting needs for the transportation org. You will be exposed to cutting edge AWS big data technologies. You should have excellent business and communication skills to be able to work with business owners and Tech leaders to gather infrastructure requirements, design data infrastructure, build up data pipelines and data-sets to meet business needs. You stay abreast of emerging technologies, investigating and implementing where appropriate.

Key job responsibilities

  1. Design and develop the pipelines required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Python and AWS big data technologies.
  2. Oversee and continually improve production operations, including optimizing data delivery, re-designing infrastructure for greater scalability, code deployments, bug fixes and overall release management and coordination.
  3. Establish and maintain best practices for the design, development and support of data integration solutions, including documentation.
  4. Work closely with Product teams, Data Scientists, Software developers and Business Intelligence Engineers to explore new data sources and deliver the data.
  5. Able to read, write, and debug data processing and orchestration code written in Python/Scala following best coding standards (e.g. version controlled, code reviewed, etc.).

Minimum Qualifications:

  1. 1+ years of data engineering experience.
  2. Experience with data modeling, warehousing and building ETL pipelines.
  3. Experience with one or more query languages (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala).
  4. Experience with one or more scripting languages (e.g., Python, KornShell).
  5. Experience with big data technologies such as: Hadoop, Hive, Spark, EMR.
  6. Experience with any ETL tool like Informatica, ODI, SSIS, BODI, Datastage, etc.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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