Data Engineer, AWS Finance Technology

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

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Are you passionate about data? Does the prospect of dealing with massive volumes of data excite you? Do you want to build data engineering solutions that process billions of records a day in a scalable fashion using AWS technologies? Do you want to create the next-generation tools for intuitive data access? If so, Amazon Finance Technology (FinTech) is for you!

FinTech is seeking a Data Engineer to join the team that is shaping the future of the finance data platform. The team is committed to building the next generation big data platform that will be one of the world's largest finance data warehouse to support Amazon's rapidly growing and dynamic businesses, and use it to deliver the BI applications which will have an immediate influence on day-to-day decision making. Amazon has culture of data-driven decision-making, and demands data that is timely, accurate, and actionable. Our platform serves Amazon's finance, tax and accounting functions across the globe.

As a Data Engineer, you should be an expert with data warehousing technical components (e.g. Data Modeling, ETL and Reporting), infrastructure (e.g. hardware and software) and their integration. You should have deep understanding of the architecture for enterprise level data warehouse solutions using multiple platforms (RDBMS, Columnar, Cloud). You should be an expert in the design, creation, management, and business use of large data-sets. You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions, and to build data sets that answer those questions. The candidate is expected to be able to build efficient, flexible, extensible, and scalable ETL and reporting solutions. You should be enthusiastic about learning new technologies and be able to implement solutions using them to provide new functionality to the users or to scale the existing platform. Excellent written and verbal communication skills are required as the person will work very closely with diverse teams. Having strong analytical skills is a plus. Above all, you should be passionate about working with huge data sets and someone who loves to bring data-sets together to answer business questions and drive change.

Our ideal candidate thrives in a fast-paced environment, relishes working with large transactional volumes and big data, enjoys the challenge of highly complex business contexts (that are typically being defined in real-time), and, above all, is a passionate about data and analytics. In this role you will be part of a team of engineers to create world's largest financial data warehouses and BI tools for Amazon's expanding global footprint.

Key job 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.
Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions.
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.
Learn and understand a broad range of Amazon’s data resources and know when, how, and which to use and which not to use.
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.
Triage many possible courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment.