Director leading strategy, governance, and delivery of enterprise data platform at Phillips 66. Partnering with AI, Data Science, and business teams to enhance analytics and business systems.
About the role
- Data Engineer enhancing data infrastructure reliability and efficiency for Love, Bonito. Involves pipeline optimization, data quality assurance, and robust operations.
Responsibilities
- Update documentation of existing data pipelines to establish clear operational standards and knowledge transfer
- Refactor legacy code to improve scalability, performance and maintainability
- Optimise aggregation layers and transformation logic for reporting workloads
- Reduce pipeline runtimes through strategic improvements in query optimisation, partitioning and resource allocation
- Review and enhance data models supporting analytics, reporting and machine learning use cases
- Design and implement efficient data structures that balance query performance with storage costs
- Collaborate with analytics and data science teams to ensure data models meet downstream requirements
- Implement comprehensive data quality checks and validation frameworks within pipelines
- Develop unit tests for data transformations to ensure correctness and prevent regressions
- Monitor data integrity throughout the pipeline lifecycle
- Establish and maintain data quality metrics and alerting mechanisms
- Troubleshoot pipeline failures and implement root cause analysis
- Design and deploy preventative measures to minimize future incidents
- Maintain pipeline uptime targets of 99%
- Ensure SLA adherence across critical data workflows
- Maintain data security, governance and compliance standards across all data assets
- Implement access controls and data lineage tracking
- Ensure adherence to regulatory requirements and internal data policies
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 2-4 years data engineering experience
- Proven track record of building and maintaining production data pipelines at scale
- Strong problem-solving skills with focus on systematic root cause analysis
- Excellent communication skills for technical documentation and cross-functional collaboration
- Core Stack
- Data Warehousing: Experience with cloud data warehouses (Redshift, BIgQuery) or lakehouse architectures
- AWS Cloud Services: Working knowledge of S3, IAM, EC and related services
- Programming: Strong Python skills with focus on PySpark, SQL, Scala for ETL
- DevOps & Engineer Practices
- Containerisation (Docker, Kubernetes)
- CI/CD pipelines and version control (Git, GitHub Actions)
- Preferred Qualifications
- Experience optimisation aggregation layers for high-volume reporting workloads
- Familiarity with Delta Lake and lakehouse design patterns
- Background in distributed computing and Spark performance tuning
- Infrastructure as code and automated deployment practices
Job title
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