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
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