Senior Data Solutions Engineer - Finance Technology

Success Booster; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Success Booster is seeking a hands-on Finance Technology Data Engineer to design, build, and support data solutions for Fund Accounting processes.

You will use SQL Server, Databricks, Python, PySpark, and APIs to deliver scalable data products and dashboards. The role requires strong data modeling, validation, and governance skills within a controlled enterprise environment.

Qualifications

• Hands-on data engineering experience in financial services or asset management.
• Strong SQL, Python, and Spark-based data transformation skills.
• Ability to design scalable data products and maintain data quality.
• Experience with data governance, lineage, and reconciliation.
• Familiarity with enterprise data platforms and reporting.
• Comfortable working in cross-functional teams and agile delivery.

Responsibilities

• Design, build, test, deploy and support Finance Technology data solutions.
• Deliver scalable solutions using SQL Server, Databricks, Python, and APIs.
• Develop validation, reconciliation, monitoring, and control capabilities.
• Create data products for dashboards, reports, and downstream apps.
• Collaborate with Finance Tech, Fund Accounting, and data governance teams.

Skills

SQL Server

Databricks

Python

PySpark

ETL/ELT

Data modeling

Data validation

Data reconciliation

Git

Deployment pipelines

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science

Tools

Azure Data Factory

Data Lake Storage

Azure DevOps

Microsoft Fabric

APIs

JSON

Job description

Role & responsibilities

Finance Technology Data Solutions

• Design, build, test, deploy, and support Finance Technology data solutions that serve Fund Accounting processes and operating requirements

-Use SQL Server, Databricks, Python, PySpark, APIs, and related technologies to deliver scalable solutions from design through production support.

Data quality, reconciliation, and controls

• Build validation, reconciliation, exception-management, monitoring, and control capabilities as part of shared delivery with Fund Accounting, Data Governance, Data Product Management, and engineering partners.
• Build and support validation, reconciliation, monitoring, exception-management, and control capabilities aligned with agreed business, data-quality, and control requirements.

Data Products & Downstream Consumption

• Build Finance Technology data products that support Fund Accounting operational processes, dashboards, reports, analytics, extracts, downstream applications, and other consumption needs.

Business partnership and engineering discipline

• Collaborate across Finance Technology, Fund Accounting, Enterprise Data Engineering, Data Governance, Data Product Management, Centralized Reporting / BI, application teams, and delivery partners throughout the delivery lifecycle.

Preferred candidate profile

Education: Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline. Equivalent relevant experience will be considered.

Experience Required: Hands-on data engineering, data solutions, or financial technology experience, ideally within financial services, asset management, accounting, investment operations, or another controlled enterprise data environment.

Must-have capabilities

• Advanced SQL Server skills, including complex queries, stored procedures, performance tuning, and troubleshooting.
• Hands-on Databricks experience, including notebooks, workflows/jobs, Spark concepts, Delta Lake patterns, and production support.
• Python and/or PySpark for transformation, validation, automation, and testing.
• Strong ETL/ELT, data modeling, integration, and curated-data-layer experience.
• Experience building domain-specific transformations, integrations, data products, validation, reconciliation, monitoring, and controls.
• Ability to own technical delivery while working effectively within a broader enterprise data and reporting operating model.
• Git, pull requests, automated testing, deployment pipelines, and disciplined software delivery.

Strongly preferred

• Azure Data Factory, Data Lake Storage, Azure DevOps, Microsoft Fabric, APIs, JSON, and secure file-transfer patterns.
• Experience with Fund Accounting systems, fund administrator data, financial balances, transactions, capital activity, or multi-system reconciliations.
• Working knowledge of metadata, lineage, catalog, master/reference data, governance, controls, and auditability concepts.
• Experience structuring governed data for dashboards, reports, extracts, downstream applications, operational workflows, and analytics.