Purpose of Onyx
The Onyx Research Data Tech organization is GSK’s$_{}$ Research data ecosystem which has the capability to bring together, analyze, and power the exploration of data at scale. We partner with scientists across GSK to define and understand their challenges and develop tailored solutions that meet their needs. The goal is to ensure scientists have the right data and insights when they need it to give them a better starting point for and accelerate medical discovery. Ultimately, this helps us get ahead of disease in more predictive and powerful ways.
Onyx is a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:
- Building a next-generation, metadata- and automation-driven data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity and reducing time spent on “data mechanics”
- Providing best-in-class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top-tier talent
- Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions in real-time
We are looking for a skilled and experienced Sr Data Platform Engineer to join our growing team. Data Platform Engineers take full ownership of delivering high-performing, high-impact data platform as products, and services, from a description of a problem customer Data Engineers are trying to solve all the way through to final delivery (and ongoing monitoring and operations). They are standard bearers for software engineering and quality coding practices within the team and are expected to mentor more junior engineers; they may even coordinate the work of more junior engineers on a large project. They devise useful metrics ensuring their services are meeting customer demand, having an impact, and iterate to deliver and improve on those metrics in an agile fashion.
The Data Platform team builds and manages reusable components and architectures designed to make it both fast and easy to build robust, scalable, production-grade data products and services in the challenging biomedical data space.
A Sr Data Platform Engineer is a technical individual contributor, building modern, cloud-native systems for standardizing and templatizing scientific data analysis and data engineering, such as:
- Cloud native infrastructure, CI/CD and DevOps (GCP and Azure)
- Data management and governance (data + metadata + versioning + provenance + governance)
- High dimensionality and scale scientific data processing and analysis with multi-modality scientific data
- API semantics and ontology management
- Standard API architecture, implementation and operation
- Standard streaming semantics and processing
- Standard components for publishing data to file-based, relational, and other sorts of data stores
- Metadata systems
- Tooling for QA / evaluation
Key responsibilities for the Senior Data Platform Engineer include:
- Given a well-specified data and scientific problem, implement end-to-end solutions using appropriate programming languages (e.g. Python, R, bash), open-source tools (e.g. Spark, Nextflow, k8s...), and cloud vendor-provided tools (e.g. gcloud cli, Azure)
- Leverage tools provided by Tech (e.g. infrastructure as code, cloud Ops, DevOps, logging / alerting, ...) in delivery of solutions
- Write proper documentation in code as well as in wikis/other documentation systems
- Write fantastic code along with proper unit, functional, and integration tests for code and services to ensure quality
- Stay up-to-date with developments in the open-source community around data engineering, data science, and similar tooling
Why you?
Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience with 8+ years of work experience
- Proficiency with Python Programming language
- Experience with public cloud providers like AWS, Azure and GCP
- Experience with agile software development and DevOsps-forward ways of working
Preferred Qualifications:
If you have the following characteristics, it would be a plus:
- Advanced Degree
- Experience building data pipelines with ETL/ELT tools and orchestration frameworks.
- Knowledge of data modeling, partitioning, and performance tuning for analytical workloads.
- Knowledge of data governance, access controls, and data quality practices.
- Prior experience mentoring engineers and collaborating with cross-functional product teams.
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