Data Analyst, Financial Data Engineering

United States Digital Space LLC; New York (NY); On-site; USD 120,000 - 150,000; Full time

Job summary

United States Digital Space LLC is seeking a data engineering professional to design, build, and scale data pipelines and dashboards powering financial analytics and reporting. You will partner with Treasury Finance, data scientists, and engineers to deliver reliable data products and insights across the organization.

You will own end-to-end data ingestion, modeling, and transformation, while establishing quality standards, tests, monitoring, and SLAs to ensure data freshness and trusted

Qualifications

• 6+ years of full-time experience in Data Engineering, Analytics Engineering, BI Engineering, or related analytical role.
• Proficiency in SQL, including complex query optimization and data modeling.
• Proficiency in Python for data pipeline development, not just scripting.
• Experience with distributed data frameworks like Spark to write and debug data pipelines.
• Experience with workflow orchestration tools (e.g. Airflow, Flyte, or equivalent).
• Proven ability to design, implement, and maintain production-grade data pipelines and dashboards.
• Good understanding of development processes and best practices like engineering standards, code reviews, and testing.
• Ability to clearly communicate results and drive impact with cross-functional partners.
• Experience owning production data products with defined quality standards, testing, and documentation.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows that power production-grade financial reporting, risk measurement, and operational decisioning for Treasury Finance
• Leverage AI tools (code assistants, LLM-based agents) to accelerate pipeline development, data quality automation, reconciliation, and documentation
• Model and transform raw data into clean, well-documented datasets that serve as the core foundations for decision making for Treasury Finance (e.g. float positions, cash explainability, risk exposures, liquidity management)
• Establish and enforce data quality standards through testing, monitoring, and alerting on pipeline health
• Establish and own data freshness SLAs, operational alerting, and incident response for your data domains - ensuring production reliability for risk and finance critical workflows
• Partner deeply with Treasury Finance, data scientists/analysts, and engineers to define data requirements and deliver trusted, reusable financial data products
• Partner deeply with Treasury Finance stakeholders to translate business requirements into data architecture decisions, anticipating needs and helping to drive data strategy
• Build self-service tooling and analytics layer that empower stakeholders to access and explore trusted data autonomously

Skills

SQL

Python for data pipelines

Data modeling

Spark

Airflow

Dashboards

Communication

Tools

Spark

Airflow

Flyte

Job description

Who we are

About the company

the company is a financial infrastructure platform for businesses. Millions of companies - from the world20 9s largest enterprises to the most ambitious startups - use the company to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone20 9s reach while doing the most important work of your career.

About the team

Data Science at the company is a vibrant community where data analysts and data scientists learn and grow together. You20 9ll work with some of the most fundamental data at the company, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across the company and Data Analysts are hired in line with the business needs and domain of the organization they will support.

What you20 99ll do

In this role, you20 9ll partner deeply with teams across the company to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You20 9ll design, build, and own the scalable data infrastructure that powers analytics and reporting across the company.

Day to day, you20 9ll translate complex business requirements into reliable data models, own end-to-end pipeline development from raw data ingestion to clean, consumption-ready datasets, and work with leaders to prioritize the highest-impact data investments. You20 9ll go beyond building dashboards—you20 9ll architect the data layer that makes self-service analytics possible and deliver actionable business recommendations through rigorous analysis and data storytelling.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows that power production-grade financial reporting, risk measurement, and operational decisioning for Treasury Finance
• Leverage AI tools (code assistants, LLM-based agents) to accelerate pipeline development, data quality automation, reconciliation, and documentation - expanding technical scope while maintaining quality.
• Model and transform raw data into clean, well-documented datasets that serve as the core foundations for decision making for Treasury Finance (e.g. float positions, cash explainability, risk exposures, liquidity management)
• Establish and enforce data quality standards through testing, monitoring, and alerting on pipeline health
• Establish and own data freshness SLAs, operational alerting, and incident response for your data domains - ensuring production reliability for risk and finance critical workflows
• Partner deeply with Treasury Finance, data scientists/analysts, and engineers to define data requirements and deliver trusted, reusable financial data products
• Partner deeply with Treasury Finance stakeholders to translate business requirements into data architecture decisions, anticipating needs and helping to drive data strategy rather than reacting to requests
• Build self-service tooling and analytics layer that empower stakeholders to access and explore trusted data autonomously

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 6+ years of full-time experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or a related analytical role
• Proficiency in SQL, including complex query optimization and data modeling
• Proficiency in Python for data pipeline development, not just scripting
• Experience with distributed data frameworks like Spark to write and debug data pipelines
• Experience with workflow orchestration tools (e.g. Airflow, Flyte, or equivalent)
• Proven ability to design, implement, and maintain production-grade data pipelines and dashboards
• Good understanding of development processes and best practices like engineering standards, code reviews, and testing
• Ability to clearly communicate results and drive impact with cross-functional partners
• Experience owning production data products with defined quality standards, testing, and documentation

Preferred qualifications

• Prior experience at a growth-stage internet or software company
• Prior experience working with Finance or Treasury teams
• Understanding of treasury and finance concepts (e.g., float positions, FX exposure, cash reconciliation, balance sheet usage, liquidity management)
• Experience with data quality frameworks, data contracts, tiering/classification, or SLA management
• Experience creating leadership-level reporting, such as QBRs and MBRs
• Experience building financial reporting infrastructure - e.g. automated treasury processes, regulatory reporting, or finance close
• Proficiency with AI tools (code assistants, LLM agents) to accelerate pipeline development and data quality automation
• Interest in how data products enable automated/agentic workflows — understanding that data quality determines the reliability of every downstream decision