Amazon Data Engineer II AWS Actuals 

United States, Texas, Dallas 

943104333

Responsibilities- Design, implement, and support a platform providing secured access to large datasets.
- Interface with tax, finance and accounting customers, gathering requirements and delivering complete BI solutions.
- Model data and metadata to support ad-hoc and pre-built reporting.- Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
- Tune application and query performance using profiling tools and SQL.
- Analyze and solve problems at their root, stepping back to understand the broader context.- Keep up to date with advances in big data technologies and run pilots to design the data architecture to scale with the increased data volume using AWS.
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for datasets.
Key job responsibilities
1. Create an operationally stable data pipeline that deals with a very large transactional volume
2. Ability to innovate on transactional data processing on a file system like use of open table format, incremental data processing, etc.
3. Understand the business domain and create dataset/data mart of leadership reporting
4. Implement data governance on the dataset including data lineage, data quality, fine grain access control, etc.
5. Innovate on the technical stack of compute, storage and visualization

- 4+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience with SQL
- Bachelor's degree
- Knowledge of distributed systems as it pertains to data storage and computing

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence