Data Engineer, Amazon Operations Finance Standardisation and Automation

at  Amazon EU SARL

• 3+ years of relational database technology (such as Redshift, Oracle, MySQL or MS SQL) experience
• 3+ years of developing and operating large-scale data structures for business intelligence analytics (using ETL/ELT processes) experience
• 2+ years experience in at least one scripting or programming language, such as Python, Scala, Ruby etc.
• Experience with Native AWS Tools and Technologies (S3, EMR , Glue , SNS,- SQS, Cloud watch, Event bridge, MWAA, lambda etc )
• Experience with MAWS Tools and Technologies used across CDO (Cradle , Data net , Andes etc )
• Experience managing competing priorities simultaneously and driving projects to completion
• Experience communicating to senior management and customers verbally and in writing
  As a Data Engineer in Data Infrastructure and Strategy group, you will play a key role in building a single source of truth datasets for WW scope of Operations Finance organization. You will work to advance 3 Year Data Infrastructure Modernisation strategy and build robust data architecture and integration between disperse amazon data sources while following cloud-first design.
  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:

Luxembourg, LUX

• 2+ years of processing data with a massively parallel technology (Spark or Hadoop based big data solution) experience
• Familiarity with data security and privacy frameworks ( GDPR , Kale ,Anvil, ASR etc)
• Experience of leading and influencing the data or BI strategy of your team
• Experience of hiring, developing and promoting engineering talent
• Experience of end to end managing delivery of data engineering solution

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates

• 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