Finance Technology Data Solutions Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Finance Technology Data Solutions Engineer based in United States.

This role offers the opportunity to build and support data solutions powering fund accounting, investment operations, and financial reporting for a US-based asset management environment. You will work across data integrations, transformations, reconciliations, controls, and data products that support critical financial processes. The position requires strong hands-on expertise with Databricks, Python, PySpark, and SQL to process and manage large-scale financial data. You will apply your understanding of fund accounting, investment data, and financial services to deliver reliable and well-controlled solutions. Working as an individual contributor, you will collaborate directly with US stakeholders and contribute to data engineering initiatives in a regulated environment. The role is available on a part-time or full-time remote basis.

Accountabilities

• Build, maintain, and support data solutions for fund accounting, investment operations, and finance reporting.
• Develop data integrations, transformations, pipelines, and data products using Databricks, Python, PySpark, and SQL.
• Implement and maintain financial data reconciliations, controls, and validation processes.
• Work with fund accounting processes including NAV calculation, accruals, corporate actions, fees, and month-end close.
• Process and integrate investment data covering positions, transactions and trades, security master data, pricing, cash, and general ledger information.
• Optimize complex SQL queries, data pipelines, and large-scale processing workloads for performance and reliability.
• Work with cloud-based data platforms and support scalable data engineering solutions.
• Collaborate directly with US-based stakeholders to understand requirements and deliver effective data solutions.
• Contribute to data quality, testing, CI/CD, and operational improvements across financial data pipelines.

Requirements

• 10+ years of hands-on data engineering experience, with a strong individual contributor focus rather than team leadership or architecture.
• Strong, recent hands-on experience with Databricks, including Delta Lake, notebooks, jobs, and workflows; Unity Catalog experience is preferred.
• Strong understanding of fund accounting processes, including NAV calculation, accruals, corporate actions, fees, and month-end close.
• Advanced Python and PySpark skills for large-scale data processing.
• Proven experience delivering data solutions within Financial Services, with Asset Management, Investment Management, Fund Administration, Custody, or Capital Markets experience strongly preferred.
• Experience developing reconciliations and data controls within financial or regulated environments.
• Strong understanding of investment data, including positions, transactions, security master, pricing, cash, and general ledger data.
• Expert SQL skills, including complex joins, window functions, and performance tuning.
• Experience with at least one cloud platform, such as Azure or AWS.
• Strong written and spoken English, with confidence communicating directly with US stakeholders.
• Databricks Data Engineer or Azure/AWS data engineering certification is a plus.
• Experience with Azure Data Factory, Airflow, Databricks Workflows, or Fivetran is beneficial.
• Familiarity with Snowflake, dbt, Power BI, or Tableau for downstream reporting is a plus.
• Experience with Git, Azure DevOps, GitHub Actions, CI/CD, and unit testing for data pipelines is beneficial.
• Finance Management and a BE, BTech, or MCA qualification are advantageous.

Benefits

• Fully remote working model.
• Part-time or full-time employment options.
• Opportunity to work on financial data solutions supporting fund accounting and investment operations.
• Direct collaboration with US-based stakeholders.
• Exposure to Financial Services and asset management data environments.
• Hands-on work with Databricks, cloud data platforms, Python, PySpark, and modern data engineering technologies.
• Opportunity to contribute to data solutions involving reconciliations, controls, reporting, and regulated financial processes.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.