Senior Consultant, Data Engineer

In DAISE, we are focused on delivering value-adding, sustainable data capabilities, aligned to our client's specific needs. This expertise is applied across clients in all of our industry market sectors (Financial Services, Products & Services, Energy & Resources, Pharmaceutical & Lifesciences and Government). What you will be doing

• Defining and implementing on premise or cloud architectures, (e.g. a cloud data warehouse, data lake or data platform) to enable digital transformation
• Working with clients on data warehousing, building operational ETL/ELT data pipelines across a number of sources, and constructing relational and dimensional data models
• Performing maturity assessments across clients' data capabilities and recommending improvements
• Building technology blueprints and advising clients on the different technology options
• Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisation's strategy, policies and standards and in some cases, help customers to define new policies, principles and standards
• Helping clients to identify risks and mitigations for their complex data programmes, as well as transition to modern cloud-based infrastructures (AWS, Azure, GCP) by leveraging related architecture patterns (e.g., APIs, events)
• Working independently with clients in key areas of data governance, such as in defining principles including, but not limited to master data management, data Lineage and Data Security
• Experience with mentoring, guiding, training and/or upskilling people on technical concepts who are earlier-on in their career from the team, and supporting their growth
  
  Passionate individual who is excited by problems with data and can bring a good mix of technical delivery and core consulting skills in client engagements
• Ability to own and run complex client engagements, interact with leaders across industry, work with senior stakeholders to help them understand and frame their problems, assess their current state, and make impactful recommendations which help shape their thinking
• Strong hands on expertise in data engineering, delivering data architectures, data pipelines and solutions that are robust and scalable using modern delivery frameworks and tools (e.g. Databricks)
• Experience in using cloud technologies (Azure, AWS, GCP) as both infrastructure and as a service, as well as big data platforms either on-premise or cloud setup
• Knowledge of different technology stacks including common legacy and modern stacks, experience of applying DevOps practices to data engineering as well as ability to build CI/CD pipelines
• Competent in SQL and at least one modern programming language, such as Python
• Understanding of key core concepts like distributed computing, batch & stream processing, functional and object-orientated programming, how pipelines are built and deployed on cloud, pipeline schedules and SLAs
• Well-versed with documentation and artefacts that need to go along with the solution design and delivery work