Prolim is hiring for a Business Systems Analyst for a leading client
REQUIRED SKILLS TO GET AN OFFER:
- Advanced SQL, including hands-on experience with large scale distributed query engines such as Presto and Hive
- Demonstrated experience building and maintaining production data pipelines (ETL/ELT), including scheduling, backfills, and failure monitoring
- Data modeling and warehouse design: table schema design, partitioning, and documentation
- Python or similar scripting for data transformation and automation
- Experience supporting IT, enterprise support, or technical operations organization
- Experience building dashboards and maintaining operational metrics or SLA reporting
Position Summary:
Business Analyst with 6-7 years of experience in SQL, data modelling, pipeline development, and operational analytics, who can own and improve the data systems supporting all Enterprise Support pillars rather than only maintain them. Day to day this covers building and maintaining data pipelines, Hive tables, SQL queries, and dashboards. Beyond that, the role is expected to bring its own judgment: proposing better data models, applying AI and LLM tooling to accelerate data work and extract signal from unstructured support data, improving documentation and governance standards, and identifying reporting gaps. Priorities and direction are set with the Meta manager, but the contractor owns how the work gets done and is expected to identify and recommend improvements.
Compliance Requirements
Must follow Meta data handling and privacy standards, including appropriate data classification, least privilege access to sensitive datasets, and no storage of confidential or personally identifiable data outside approved internal systems. Must apply sound judgment about the appropriate use of AI and LLM tooling on internal data. Experience working within enterprise data governance and access control processes is required.
Skillsets/Qualifications:
- Helpful qualifications include:
- Advanced SQL, ideally with Presto and Hive against large datasets
- Building and maintaining production data pipelines: scheduling, dependency management, backfills, and monitoring
- Data modeling and warehouse design: schema design, grain, partitioning, and documentation
- Practical use of AI and LLM tooling in data work, and judgment about where it is and is not appropriate
- Data governance practice: documented metric definitions, clear table ownership, schema change management, and retiring stale datasets
- Python or similar scripting for data transformation and automation
- Dashboarding and data visualization
- Prior experience supporting an IT, enterprise support, or operations organization
- Self-directed: able to take a loosely defined problem, propose an approach, and execute it
- Bachelor's degree in a quantitative, analytics, computer science, or information systems field, or equivalent practical experience
Responsibilities:
- Own the day-to-day health of the Enterprise Support data layer: pipelines, Hive tables, SQL, and dashboards
- Design and build data models for new reporting needs, proposing the approach rather than receiving a specification
- Maintain a documented source of truth for Enterprise Support metrics: definitions, ownership, and freshness
- Apply AI and LLM tooling to accelerate data work and to extract signal from unstructured support data such as ticket notes and resolution summaries
- Improve pipeline reliability and reduce manual effort over time through automation
- Monitor pipeline health, resolve failures and data quality issues, and run backfills
- Deliver recurring reporting (weekly, monthly, quarterly) and ad hoc analysis
- Apply and maintain standards for naming, documentation, and schema change management
- Proactively surface data quality, governance, and coverage gaps
Minimum qualifications:
- 6-7 years of experience in data analytics, business analytics, or analytics engineering
- Advanced SQL skills, including experience with large scale distributed query engines such as Presto and Hive
- Demonstrated ownership of production data pipelines and data models, including design decisions rather than only maintenance
- Practical experience applying AI or LLM tooling within data workflows
- Experience with data modeling and dashboard development
- Ability to work independently and communicate status, recommendations, and blockers clearly in writing
Preferred qualifications:
- Data governance experience: metric catalogs, lineage, table ownership, and schema change management
- Experience supporting an IT, enterprise support, or technical operations organization
- Python or similar scripting experience for data automation
- Experience maintaining operational metrics and SLA reporting
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