Methods Analytics is currently recruiting for a SeniorData Engineer to join our teamon a permanent basis. This role will be mainly remote, but will require flexibility to travel to client sites, and our officesbased in Sheffield, Bristol and London at least once a month.
About Methods Analytics:
Methods Analytics exists to improve society by helping people make better decisions with data. Combining passionate people, sector-specific insight and technical excellence to provide our customers an end-to-end data service. We use a collaborative, creative and user centric approach data to do good and solve difficult problems. We ensure that our outputs are transparent, robust and transformative.
We value discussion and debate as part of our approach. We will question assumptions, ambition and process but do so with respect and humility. We relish difficult problems, and overcome them with innovation, creativity and technical freedom to help us design optimum solutions. Ethics, privacy and quality are at the heart of our work and we will not sacrifice these for outcomes. We treat data with respect and use it only for the right purpose. Our people are positive, dedicated and relentless. Data is a vast topic, but we strive for interactions that are engaging, informative and fun in equal measure. But maintain a steely focus on outcomes and delivering quality products for our customers.
Requirements
Ideal Candidates will demonstrate:
- Extensive experience in designing and delivering data pipelines.
- Have experience leading or mentoring a team of data engineers.
- At least one recent year of experience designing and building Azure Data Factory data flows.
- At least one recent year of experience ingesting data into an Azure Data Lake.
- At least one recent year designing data models for large datasets, including creating dimensional models as outputs for ingested data.
- Understanding of the purpose of Data Catalogues.
- Strong T-SQL Development including debugging amp; troubleshooting.
- Solid Relational Database design skills with an eye for performance optimisation.
- an ability to translate business requirements into technical specifications.
- Attention to detail and ability to QA own and other team member's work.
The Senior Data Engineer will be:
- Working with other members of the delivery team across a mix of large and small projects and be responsible for translating data into valuable insights that inform decisions for small to large transformation projects and programmes.
- Working with other members of the delivery team you will be responsible for identifying and using the most appropriate analytical techniques, developing fit-for-purpose, resilient, scalable and future-proof data services that meet user needs and design and write and iterate code from prototype to production-ready.
- Communicating effectively across organisational, technical and political boundaries to understand the context and how to make complex and technical information and language simple and accessible for non-technical audiences.
- Working with the Lead Data Engineer to support the growth and development of the team.
- Aware of and keep up to date with advances in digital analytics tools and data manipulation products.
- Collecting, collating, cleansing, synthesising and interpreting data to derive meaningful and actionable insights.
- Understanding of how to expose data from systems (for example, through APIs), link data from multiple systems and deliver streaming services.
- Producing data models and understand where to use different types of data models.
- Provide training, support and mentoring to a team of Data Engineers and helping to grow the Engineering team.
- Ensuring that risks associated with deployment are adequately understood and documented.
Other Desirable Skills amp; Experience:
- Experience in SQL Server Integration Services (SSIS).
- Good experience with ETL
- SSIS, SSRS, T-SQL (On-prem/Cloud),
- Experience with NoSQL type environments, Data Lakes, Lake-Houses (Cassandra, MongoDB or Neptune).
- Have experience with Python.
- Have cloud based configuration experience, preferably with Azure.
- Knowledge of statistics principles necessary to interpret data and apply models. For example, knowledge of errors and confidence intervals to understand whether a relation seen in the data is real.
- Exposure to high performing, low latency or large volume data systems (i.e. 0.5 billion+ records, terabyte size database).
- Working within a continuous integration environment with automated builds, deployment and unit testin