Data Engineer, Amazon Operations Finance Standardisation and Automation
1 month ago
Your responsibilities will include ownership over end-to-end implementation of data pipelines with laser focus on data modeling, standardization and accuracy: component design, implementation using NAWS services that follows best engineering practices, user acceptance testing, launch, adoption and post-launch support. You may work on system design and integrate new components to established architecture. You will be engaged into cross-team collaboration by building reusable design patterns and components and adopting designs adopted by others. You will evaluate “buy” vs “build” options that support the speed and quality of data producing. You will constantly evaluate other team’s success stories and latest product and features releases for NAWS and internal products, perform gap analysis and define feasibility of their adoption and the list of blockers.
The ideal candidate possess a track record of creating efficient AWS-based data solutions; data models for both relational databases and Glue/Athena/EMR stack; developing solution documentation, project plans, user guides and other project documentation.
Key job responsibilities
* Design, review and implement data solutions that support WW Operations Finance standardisation and automation initiatives using AWS technologies and internally built tools, including Spark/EMR, Redshift, Athena, DynamoDB, Lambda, S3, Glue, Lake Formation, etc;
* Ensure the speed of delivery and high-quality: iteratively improve development process and adopt mechanisms for optimisation of the development and support;
* Contribute into engineering excellence by reviewing design and code created by others;
* Contribute to delivery execution, planning, operational excellence, retrospectives, problem identification, and solution proposals.
* Evaluate various options in ambiguous situations using technical understanding ,deep dive and business acumen.
* Support data solutions adoption by both, finance and technical teams; identify and remove adoption blockers;
* Collaborate with finance analysts, engineers, product and program managers and external teams to influence and optimize the value of the delivery in data platform;
* Create technical and customer-facing documentation on the products within the platform;
* Perform ongoing product support ;
* Moderate travel (< 25%) may be expected.
A day in the life
You work with the Engineering, Product, BI and Operations teams to implement best-of-class data solutions for world wide operation finance organization. You solve unstructured customer pain points with technical solutions, you are focused on users productivity when working with the data. You participate in discussions with stakeholders to provide updates on project progress, gather feedback, and align on priorities. Utilizing AWS CDK and various AWS services, you design, execute, and deploy solutions. Your broader focus is on system architecture rather than individual pipelines. You regularly review your designs with Principal Engineer, incorporate gathered insights. Conscious of your impact on customers and infrastructure, you establish efficient development and change management processes to guarantee speed, quality and scalability of delivered solution.
About the team
WW Operations Finance Standardization and Automation improves customer experience and business outcomes across Amazon Operations Finance through innovative technical solutions, standardization and automation of processes and use of cutting-edge technology.
We are open to hiring candidates to work out of one of the following locations:
London, GBR
BASIC QUALIFICATIONS
- Experience with data modeling, warehousing and building ETL pipelines- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience as a Data Engineer or in a similar role
PREFERRED QUALIFICATIONS
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR- Experience operating large data warehouses
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