IT Data Engineering JG3

Data Engineering Lead

Manages one or more data engineering projects of moderate complexity. Leads or acts as key team member in defining and builds the data pipelines that will enable faster, better, data-informed decision-making within the business. Jointly designs and implements a new big data solution which supports high amounts and velocity of data and supports future growth. Identifies latest development, testing and deployment techniques to quickly deploy new releases to e.g. deploy new data pipelines and add data sources. Takes direct reports, when applicable, through Agile Framework and its respective tools. Influences stakeholders in adopting analyses / outcomes. Acts proactively to support the business to further professionalize their MI / Analytic reporting platform, using the ESSA approach. Opinions valued by business interface. Plays a prominent role in project related meetings etc. Utilises business partnering in order to gather, analyse and model data and key performance indicators of the highest complexity. Working as Data Engineering Lead to develop and deploy innovative big data platforms for advanced analytics and data processing. Works independently under broad managerial supervision.

The Data Engineer will work with our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company's data architecture to support our next generation of products and data initiatives.

Responsibilities for Data Engineer:

• Create and maintain optimal data pipeline architecture.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and Azure, AWS 'big data' technologies.
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
• Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Keep our data separated and secure across national boundaries through multiple data centers and Azure, AWS regions.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics experts to strive for greater functionality in our data systems.

Qualifications for Senior Data Engineer:

• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing 'big data' data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large disconnected datasets.
• Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
• Experience supporting and working with cross-functional teams in a dynamic environment.
• We are looking for a candidate around 5 years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
• Experience with Azure: ADLS, Databricks, Stream Analytics, SQL DW, COSMOS DB, Analysis Services, Azure Functions, Serverless Architecture, ARM Templates
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with object-oriented/object function scripting languages: Python, SQL, Scala, Spark-SQL