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Datonomy has 20 years’ experience in data and digital solutions, we are perfectly placed to catapult you onto the career path of your dreams. Our projects cover the full spectrum of data; therefore, we can offer you access to the most interesting, innovative, and ground-breaking work currently being done.

Our goal is to positively impact One Billion People by 2030 – and we can only achieve this if our clientsand our people collaborate on projects and solutions that will change the world.

AWS Data Engineer

Purpose of Job/Role:

The Data Engineer role, part of the Advanced Analytics Centre of Excellence (CoE) will provide strategic technology input and be responsible to build, maintain and implement a world class data science platform and systems that will enable and empower the advanced analytics capability to deliver on its strategic mandate; delivering insights, models, and data products to the wider business to drive better business outcomes and customer engagement.

Performing a consultative role within the Advanced Analytics CoE and the wider BI and technology division to create a sustainable data science platform within AWS. Model and insight productionisation will also be part of responsibility of the Data Engineer.

This is a super exciting opportunity to shape our design solution thinking as we endeavour to better understand and partner with our customers to meet their needs, build their trust and optimise the potential of our offerings.

Qualifications and experience

Minimum Qualifications & Experience:

• Bachelor's degree or equivalent combination of education and experience.
• 1-3 Years Python, Spark, PySpark
• 3 to 5 years’ experience in software engineering with a focus on serverless applications.
• Proven experience of core AWS services and features:
• AWS Lambda
• Amazon Athena
• AWS Step Function
• DynamoDB
• Amazon Cognito
• S3 Buckets
• Amazon API Gateway
• CloudWatch
• Redshift
• Amazon RDS
• IAM
• KMS
• AWS DevOps Engineer – Professional’ or ‘AWS SysOps Administrator – Associate’ is advantageous.
• Understanding of Retail is beneficial.
• Understanding of Advanced Analytics/Data Science platforms beneficial
• Knowledge of SAP and BW / Business Intelligence systems is helpful.

Details of the Job/Role:

Data Engineering:

Part of the Business Intelligence team. Expected to be an AWS Cloud developer, facilitator, and evangelist, working and consulting with various members of the BI, Architecture, Networks, Cloud and Infrastructure teams as well as key business stakeholders. Passionate about maintaining and developing Cloud-based solutions and processes to improve ways of working and utilisation of technology to ultimately help the Advanced Analytics CoE deliver data products.

Core Technical Responsibilities and Requirements:

• Programmatically create infrastructure in AWS by designing, developing and scaling infrastructure-as-code. Ensure the systems are highly available and resilient.
• Build scalable and secure data mart and pipelines, often performing complex calculations with massive volumes of data from various sources to summarise various entity dynamics.
• Enable the delivery of new code and infrastructure changes through Continuous Integration and Deployment Pipelines.
• Protect our customer data with continuous security practices and monitoring.
• Working with business stakeholders, BI team members and other IT Teams to define and implement strategic objectives.
• Guiding Business Intelligence architecture users on the journey towards Cloud-based solutions.
• Detailed knowledge of delivery life cycle associated with AWS Cloud-based solutions including but not limited to the key phases, core milestones, critical success factors, testing, typical challenges, and mitigation strategies.
• Working with the architectural and solution delivery functions to establish detailed and achievable delivery plans within an Agile delivery framework.
• Able to work on simultaneous assignments/complex projects.
• Researching, evaluating, and recommending new and better ways of doing things based on experience, technology, and market trends.
• Working with business, technical, analytics, data science and third -party communities to define, entrench and evolve Cloud-based solutions while forging better ways of working.
• Supporting and sound boarding with team members.
• Define and document BI Cloud solution architecture including technical designs and diagrams.
• Document or update specifications for system developments and work with developers and data engineers on requirements.
• Where needed, provide first and second line incident support to the Advanced Analytics CoE and business by analysing and clarifying the issue and resolving issues within the agreed operational and service level agreement (OLA/SLA) and communicating effectively to business users and colleagues.
• Escalate issues timeously to the BI Support Team Lead and/or BI Technical Capability Lead for support when a solution is not forthcoming.
• Where needed, actively monitor processes (e.g. daily batch jobs in the system).

Analytics:

The role is instrumental in the delivery of data analytics products, initially focused on marketing and customer initiatives but ultimately to the entire organisation and its wider stakeholder and partner groups. Having an interest and understanding of how insights, analytics and data products deliver value to an organisation and having a passion for analytics in general will greatly increase efficacy of the data engineer. This passion will also translate into researching and implementing the latest tooling to enable best of class deliveries.

Collaboration and Communication:

The data science team will build solutions for business from data stored in production. An ability to work with all parties and particularly in delivering to business while utilising the frameworks of the data warehousing teams, including hardware and software deployed, will enable efficient and scalable data products.

Communication and partnering with these teams to deliver analytics services of value will be key. More often than that the role will require collaboration within the analytics and data warehousing community. Being known as a team player for the Advanced Analytics CoE and other teams will greatly enhance the trust placed in the CoE. Reporting, report building and feedback, to stakeholders will also be required as part of continuous communication to stakeholders and partners.

Excellence:

Data Science products must be built in a scalable way and in a manner that allows for quality control; the data engineer will play a vital role in enabling scale and quality control in production systems. The role would require to always search for ways to make the data system more efficient and looking for new data sets or analytics techniques that would create better business value for the Advanced Analytics CoE. The role would also require the utmost respect and protection of all customer and company data and calling out and finding ways to mitigate risks related to it or any other data pipeline risks.

Personal:

Data Science and analytics requires a great level of being comfortable with the unknown, being creative with solutions and understanding that small pieces of information can greatly contribute to the bigger picture. The role requires being inquisitive, creative, and focused on the implications of decision making of the products and services delivered by the Advanced Analytics CoE, which will ultimately lead to well-designed products and services. A hands-on, can do attitude will be required to make sense of the unknown.

Self-driven, curious, passionate, problem-solver (root causes), thorough, detailed, accurate, responsible/ accountable, self-starter and team player. Communication skills, social skills/relationship-building, influencing skills, documentation skills, analytical skills, consulting skills, training skills, and presenting skills.