Data Engineer, AWS Support S&O - ESSO - Strategy, Planning & Inspection (SPI)
Amazon Quicksight
Amazon Web Services
AWS
Aws Glue
Aws Iam
Big Data
Bigdata
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Integration
Data Lake
Data Pipeline
Data Platform
Data Processing
Data Warehouse
Database
Databases
EMR
Engineer
ETL
Firehose
Redshift
Reporting and Analytics
S3
SQL
Job Description
Amazon Web Services, Inc. offers an onsite Data Engineer role in Pittsburgh that combines a competitive salary with a strong benefits package and opportunities to influence AWS Enterprise Support Strategy and Ops. The position pays between USD 132,100 and 178,800 per year and places you in a collaborative environment that values data-driven decision making and cross-team collaboration with data scientists and business intelligence engineers.
Benefits
- Health insurance
- 401(k) matching
- Paid time off
- Parental leave
- Sign-on payments
- Restricted stock units (RSUs)
Responsibilities
- Design and maintain an analytical data infrastructure
- Manage AWS resources including EC2, EMR, S3, Glue, Redshift, and related services
- Collaborate with other technology teams to extract, transform, and load data from diverse sources using SQL and AWS big data technologies
- Stay current with the latest AWS capabilities to deliver new features and improve efficiency
- Work with Data Scientists and Business Intelligence Engineers to promote best practices in reporting and analysis
- Continuously enhance reporting and analysis processes to simplify self-service access for customers
Requirements
- Bachelor's degree
- Experience as a data engineer or related specialty with a track record of manipulating, processing, and extracting value from large datasets
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL
- Experience providing technical leadership and mentoring other engineers on data engineering best practices
- Experience building and operating highly available, distributed systems for data extraction, ingestion, and processing of large data sets
- Experience with data modeling, warehousing, and building ETL pipelines
Technologies
- SQL
- Redshift
- Quicksights
- EC2
- EMR
- S3
- Glue
- Kinesis
- FireHose
- Lambda
- IAM