Onsite | Finance Data Platform Engineer — $55–$80/hour

We are sharing a specialised full-time consulting opportunity for US-based data engineers with hands-on experience in PySpark, distributed data processing, advanced SQL, and production-grade financial data infrastructure.

This role supports the Finance organisation of a fast-growing AI company. Selected engineers will build and maintain the pipelines used by the CFO team, translate evolving financial requirements into reliable data systems, resolve production issues, and maintain accurate, timely data delivery across critical Finance workflows.

Key Responsibilities

Financial Data Pipeline Development

• Build and maintain scalable PySpark pipelines supporting Finance operations and decision-making
• Process, transform, and integrate large volumes of structured financial data
• Ensure scheduled data workflows run accurately, reliably, and on time
• Develop maintainable infrastructure that supports evolving Finance requirements

SQL Engineering & Data Validation

• Write complex SQL queries to extract, join, transform, and validate financial datasets
• Optimise query performance across large-scale and distributed data environments
• Develop validation checks that identify missing, duplicated, inconsistent, or inaccurate records
• Reconcile data across multiple systems and investigate discrepancies

Data Quality & Production Support

• Monitor pipeline performance, reliability, and data quality from source to final output
• Diagnose and resolve failed jobs, delayed workflows, and unexpected data issues
• Take end-to-end ownership of production incidents and corrective actions
• Implement safeguards that reduce recurring failures and improve operational stability

Finance & Engineering Collaboration

• Partner with Finance and engineering stakeholders to clarify ambiguous data requirements
• Translate business needs into practical data models, pipelines, and technical solutions
• Communicate technical constraints, dependencies, risks, and delivery timelines clearly
• Work independently while contributing to a small, fast-moving technical team

Ideal Profile

Strong candidates may have:

• Approximately 2–4 years of professional experience as a Data Engineer
• Hands-on production experience with PySpark
• Strong knowledge of distributed and large-scale data processing
• Advanced SQL skills across complex joins, transformations, aggregations, and validation workflows
• Experience building and supporting reliable production data pipelines
• Strong debugging skills and a practical approach to resolving data-quality issues
• Ability to translate loosely defined business requirements into working infrastructure
• Strong written communication and the ability to work independently
• Availability to work onsite full-time in one of the designated locations

Educational Background

• A bachelor's degree in computer science, software engineering, data engineering, information systems, or a related technical field is required
• Coursework or professional training in databases, distributed systems, data architecture, or software engineering is highly relevant
• Advanced technical education in data platforms or large-scale computing may be helpful
• Equivalent specialised experience may be considered where appropriate

Nice to Have

• Experience supporting Finance, accounting, FP&A, treasury, or corporate reporting data
• Familiarity with financial data models, reconciliation workflows, and controlled data environments
• Experience with cloud-based data platforms, orchestration tools, or distributed compute systems
• Knowledge of pipeline monitoring, automated testing, and data-quality frameworks
• Experience working within a high-growth technology company or rapidly evolving organisation
• Familiarity with Git, software-development practices, and production deployment workflows
• Experience partnering directly with senior Finance stakeholders

Why This Opportunity

• Build the core data infrastructure used by a fast-moving Finance organisation
• Take ownership of meaningful production pipelines and financial data workflows
• Work directly with Finance and engineering stakeholders on high-priority initiatives
• Solve complex data-quality, scalability, and operational reliability challenges
• Join a focused onsite engagement with competitive hourly compensation and extension potential

Contract Details

• Full-time W-2 contingent employment arrangement
• Initial engagement expected to last approximately six months
• Potential extension based on performance and business requirements
• Onsite role based in San Francisco, California; New York, New York; or Bellevue, Washington
• Competitive rates between $55–$80 per hour depending on expertise and location
• Candidates must be based in the United States and able to work onsite full-time
• The selection process is expected to include two technical assessments focused heavily on SQL
• This is a hands-on data engineering position focused on pipelines and infrastructure
• Responsibilities do not centre on business intelligence, dashboard creation, reporting, or machine-learning model development

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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