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

24-Mag•New York, NY

3d•Onsite

About The Position

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.

Requirements

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
A bachelor's degree in computer science, software engineering, data engineering, information systems, or a related technical field is required
Candidates must be based in the United States and able to work onsite full-time

Nice To Haves

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

Responsibilities

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
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
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
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