Data Engineer I - Data Warehouse & Self-Service Analytics

Amazon; New York (NY); On-site; USD 111,000 - 185,000; Full time

Job summary

Amazon Advertising is seeking a data engineer to design and operate data warehouse and data lake infrastructure using AWS big data stack and Python. You will build and maintain ETL pipelines, source diverse data and create unified models for analytics and reporting.

You will empower internal customers with self-serve analytics, balance many requests, and collaborate across teams while mentoring other engineers to strengthen the team culture.

Qualifications

• 1+ years of data engineering experience.
• Experience with data modeling, warehousing and building ETL pipelines.
• Familiarity with query languages (SQL, HiveQL, SparkSQL) and scripting (Python, KornShell).
• Experience with big data tools like Hadoop, Hive, Spark, EMR.
• Experience with ETL tools such as Informatica, ODI, SSIS, or Datastage.

Responsibilities

• Design, implement, and support data warehouse / data lake infrastructure using AWS big data stack, Python, Redshift.
• Develop and manage ETLs to source data from various data lakes and create unified data model for analytics and reporting
• Empower technical and non-technical, internal customers to drive their own analytics and reporting (self-serve reporting) and support ad-hoc reporting when needed.
• Develop deep understanding of vast data sources and know exactly how, when, and which data to use to solve particular business problems.
• Manage numerous requests concurrently and strategically, prioritizing when necessary collaborate across teams to deliver results.
• Mentor other engineers, influence positively team culture, and help grow the team.

Skills

Data warehousing

ETL pipelines

SQL

Python

Data modeling

Tools

Hadoop

Hive

Spark

EMR

Informatica

ODI

SSIS

Job description

Amazon Advertising is seeking a data engineer to design and operate data warehouse and data lake infrastructure using AWS big data stack and Python. You will build and maintain ETL pipelines, source diverse data and create unified models for analytics and reporting.

You will empower internal customers with self-serve analytics, balance many requests, and collaborate across teams while mentoring other engineers to strengthen the team culture.