Develop automated controls, monitoring, observability, and remediation frameworks to ensure data quality, integrity, and operational resilience
Partner with Product, Engineering, and domain experts to translate business needs into scalable technical solutions and influence platform architecture and evolution
Apply automation, AI/ML, and advanced analytics to enhance data ingestion, enrichment, validation, and monitoring processes
Establish engineering best practices and mentor team members to drive technical excellence, code quality, and operational discipline
You’ll need to have
Bachelor’s degree (or higher) in a STEM field such as Statistics, Computer Science, Quantitative Finance, or equivalent practical experience
Minimum 4 years of experience designing and building scalable data solutions, including ETL/ELT pipelines, data workflows, and automation frameworks
Experience with Python and querying structured, semi-structured, and unstructured data
Experience with modern data operations, including data modeling, governance, lifecycle management, observability, monitoring, alerting, and reliability engineering
Strong communication and stakeholder management skills, with the ability to collaborate effectively across Data, Engineering, Product, and business teams
Fluency in both spoken and written English
Demonstrated continuous career growth within an organization
We’d love to see
Experience with Bloomberg data products and workflows, plus AI/ML-enabled solutions such as anomaly detection, NLP, classification, or LLM-assisted workflows
Familiarity with modern data engineering and delivery practices, including cloud platforms, DataOps, CI/CD, metadata management, Agile delivery, and project management tools; CDMP certification is a plus
If this sounds like you
Apply if you think we're a good match. We'll get in touch to let you know what the next steps are, but in the meantime feel free to have a look at this: https://www.bloomberg.com/professional
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