Data Engineer 3 - AWS & Python (Contractual)

Key Skills

Algorithms, AWS, Big Data Technology, Data Analytics, Data Governance, Data management, Data Modeling, Data pipelines, Data science techniques, Data structuring, Data Transformation, Econometrics, ETL/ELT technologies, Information science, Lambda, Machine learning techniques, Mathematics, Metadata, MySQL, Python Programming, SSIS, Star schema, Statistics, T-SQL

Job Description

The Economist Intelligence Unit (EIU) is a world leader in global business intelligence. We help businesses, the financial sector and governments to understand how the world is changing and how that creates opportunities to be seized and risks to be managed.

At our heart is a 50 year forward look, a global forecast of the majority of the world’s economies, we seek to analyse the future and deliver that insight through multiple channels and insights, allowing our clients to take better trading, investment and policy decisions.

We’re changing, embedding alternate data sources such as GPS and satellite data into our forecasting, products will increasingly be tailored to individual clients, driven by some of the most innovative data in the market. A highly collaborative team of Product Managers, Customer Experience and Product Engineering is being created with a focus on creating business and customer value driven by real time analytics alongside our traditional products.

What will you experience

At Economist Intelligence Unit (EIU) we believe having the right work-life balance is super important; striking balance between your personal and professional life is critical to wellbeing and happiness. We offer flexible working and have recently shifted to a 'remote first' working policy with a minimum expectation of coming to the office two days a month, however you can come in more often if you wish to.

Accountabilities

How you will contribute:

• Build data pipelines: Architecting, creating and maintaining data pipelines and ETL processes in AWS via Python, Glue and Lambda
• Support and Transition: Support and optimise our current desktop data tool set and Excel analysis pipeline to a transformative Cloud scale Big Data Architecture environment. 
• Work in an agile environment: within a collaborative agile product team using Kanban
• Collaborate across departments: Work in close relationship with data science teams and with business (economists/data) analysts in refining their data requirements for various initiatives and data consumption requirements.
• Educate and train: Required to train colleagues such as data scientists, analysts, and stakeholders in data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
• Participate in ensuring compliance and governance during data use: To ensure that the data users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives.
• Become a data and analytics evangelist: This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.

Experience, skills and professional attributes

To succeed in this role it would be an advantage if you possess:

• Experience with programing in Python, and Lambda functions
• Knowledge of building bespoke ETL solutions, and extracting data using Data APIs
• MS SQL Server (data modelling, T-SQL, and SSIS) for managing business data and reporting
• Prior experience in design and developing microservice architecture
• Ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management.
• A combination of IT skills, data governance skills, analytics skills and economics knowledge
• An advanced degree in computer science (MS), statistics, applied mathematics (Ph.D.), information science (MIS), data management, information systems, information science (postgraduation diploma or related) or a related quantitative field or equivalent work experience.
• Experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms.