Data Eng II (PnD), IT PnD Data Engineering

Would you like to be the go-to Data Engineer for a team that constantly learns new domains, tools and data sources to identify best practices from across Amazon’s broad offerings and propagate them globally?

IT PnD Data Engineering team is looking for a Data Engineer to join the team to design, manage, and continuously enhance our data and analytics infrastructure, with process automation/improvements, developing automated reporting solutions/tools, and improving the ability of the IT Services organization to process, analyse, access and consume accurate and timely data.

As a Data Engineer you will be working in one of the largest cloud-based data lakes. You should have experience in the design, creation, management, and business use of extremely large datasets. You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions, and build data sets that answer those questions. You should be able to architect highly efficient data and reporting structures, making a trade-off between scalability, performance and user functionality needs, using expert knowledge in software development technologies. You should be able to lead design reviews and offer feedback on design, integration, performance and scalability issues. Serve as an authority in the area of technical and domain expertise and mentor, develop, and train data engineers.

Key job responsibilities
In this role you will work with a team in building near real time data ingestion, calculation engines, and reporting solutions for IT Services organization. The role requires someone who loves data, understands enterprise information systems, has a strong business sense, and can collaborate with multiple teams to put these skills into action. The ideal candidate thrives in a fast-paced environment, relishes working with ambiguity, big data, and enjoys the challenges of highly complex business context. This role requires an individual with a strong data modelling background, deep knowledge of cloud infrastructure, optimizing data for business intelligence solutions, and the ability to quickly learn, adapt and work with a variety of technologies.

BASIC QUALIFICATIONS

- 3+ years of data engineering experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using each of the following: ETL (Extract, Transform, Load)/ELT (Extract, Load, Transform) processes experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
- Experience with big data processing technology (e.g., Hadoop or ApacheSpark), data warehouse technical architecture, infrastructure components, ETL, and reporting/analytic tools and environments
- Knowledge of programming or other scripting languages and coding skills (C/C++/C#, Node.JS, Java, Python, PHP, Ruby)
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders

PREFERRED QUALIFICATIONS

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations

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