Join us as a Data Platform Engineering Lead
- This is an exciting opportunity to use your technical expertise to collaborate with colleagues and build effortless, digital first customer experiences
- You’ll be simplifying the bank through developing innovative data driven platform and framework solutions, inspiring to be commercially successful through insight, and keeping our customers’ and the bank’s data safe and secure
- Participating actively in the data platform engineering community, you’ll deliver opportunities to support our strategic direction while building your network across the bank
What you'll do
We’ll look to you to demonstrate technical and people leadership to drive value for the customer through platform engineering, toolkit adoption, cloud integration, sourcing and data transformation frameworks. You’ll be working closely with core technology and architecture teams to deliver strategic data solutions, while driving Agile and DevOps adoption in the delivery of data engineering, leading a team of platform engineers.
We’ll also expect you to be:
- Ensuring deployment and management of distributed data platforms
- Product licensing , adoption and integration
- Owning and Delivering the automation of data engineering pipelines
- Developing frameworks to enable bank’s data structures and metrics, advocating change where needed for product development
- Educating and embedding new tools and techniques into the business through role modelling, training and experiment design oversight
- Delivering platform roadmap and engineering strategies to build a scalable data architecture and customer feature rich dataset for data scientists
- Developing solutions for streaming data ingestion and transformations in line with streaming strategy
The skills you'll need
We’re recruiting for multiple roles across a range to levels, up to and including experienced Managers.
To be successful in this role, you’ll need to be an expert level programmer and data engineer with a qualification in Computer Science or Software Engineering. You’ll also need a strong understanding of data usage and dependencies with wider teams and the end customer, as well as extensive experience in extracting value and features from large scale data.
You’ll also demonstrate:
- Deploying and managing distributed data platforms like Spark, Hadoop, Kafka, MongoDB & Neo4J
- Experience in managing Data Science and Engineering tooling on the cloud such as Sage Maker, ML Ops, Airflow, Stream Sets & Informatica
- Experience in deploying applications to at least one major public cloud provider like AWS, Azure or GCP
- Expertise in Unix and DevOps automation tools like Terraform & Puppet
- Knowledge and experience of architecting on the cloud using Site Reliability Engineering and Security principles
- Experience of ETL technical design, automated data quality testing, QA and documentation, data warehousing, data modelling and data wrangling
- Extensive experience using RDMS, ETL pipelines, Python, Hadoop and SQL
- A good understanding of modern code development practices
- Good critical thinking and proven problem-solving abilities
It would be ideal if you have experience of using Oracle, Unix scripting, Java, cloud, API, NoSQL and Kafka.