Snowflake Technical Lead – Financial Applications

Job Description – Snowflake Technical Lead – Financial Applications

About Us

Atlas Systems Inc. is a Software Solutions company headquartered in East Brunswick, NJ. Incorporated in 2003, Atlas provides solutions in GRC, Technology, Procurement, Healthcare Provider and Oracle to customers across the globe.

For more information, please visit our company website.

Please click on the link below to apply for this position:

https://atlas.bamboohr.com/careers/622

Job Title: Snowflake Technical Lead – Financial Applications

Location: Bangalore / Chennai (Onsite / Hybrid)

Experience: 8–12+ Years

Employment Type: Full-Time / Long-Term Engagement

Domain: Financial Services / Private Equity / Investment Management

Position Summary

We are seeking an experienced and hands-on Snowflake Technical Lead to lead the design, development, integration, optimization, and production support of enterprise data solutions supporting financial applications.

The ideal candidate will combine deep expertise in Snowflake, SQL, data engineering, ETL/ELT and cloud data platforms with strong functional and technical understanding of financial-services applications and their underlying data.

Direct hands-on experience with Investran is mandatory. The candidate must have worked with Investran data structures, database schemas, interfaces, reporting requirements, integrations, or downstream data platforms and should be capable of independently analyzing financial data originating from Investran.

This is a hands-on technical leadership role requiring the individual to architect solutions, develop complex data pipelines, troubleshoot production issues, perform technical reviews, and mentor engineering teams.

Key Responsibilities

Snowflake Architecture & Technical Leadership

• Lead architecture, design and implementation of enterprise-scale Snowflake data solutions.
• Define scalable data architecture covering ingestion, transformation, storage, consumption and archival.
• Design appropriate databases, schemas, tables, views, secure views, stages and data-sharing mechanisms.
• Establish engineering standards for Snowflake development, deployment, security and performance.
• Perform technical design and code reviews and mentor Snowflake/data engineering team members.
• Remain hands-on and independently troubleshoot complex production and data issues.

Investran & Financial Applications – Mandatory

• Work directly with Investran data, databases, interfaces and downstream reporting/data platforms.
• Understand Investran data structures supporting private equity, fund accounting and investment-management processes.
• Analyze and map Investran data into enterprise Snowflake models.
• Develop and support pipelines extracting and transforming data from Investran into Snowflake.
• Understand financial entities and relationships including funds, investments, investors, commitments, capital calls, distributions, transactions, valuations and accounting data.
• Reconcile source financial information against Snowflake and downstream reporting systems.
• Work with Finance, Operations, Investment and Technology stakeholders to translate financial requirements into technical data solutions.
• Support integration with other financial applications and enterprise data sources.

Candidates without direct Investran experience should not be considered for this position.

Data Engineering & ETL/ELT

• Design and develop robust ETL/ELT pipelines for structured and semi-structured financial data.
• Develop complex SQL transformations, stored procedures, views and reusable data components.
• Implement Snowflake capabilities including Streams, Tasks, Snowpipe, Dynamic Tables and Time Travel, where appropriate.
• Build batch and near-real-time ingestion patterns.
• Design data models supporting operational reporting, analytics and downstream applications.
• Implement automated data-quality validation, reconciliation, exception handling and audit controls.

Performance & Cost Optimization

• Optimize complex Snowflake queries and workloads.
• Analyze query profiles and identify performance bottlenecks.
• Optimize warehouse sizing, clustering, caching and compute utilization.
• Establish monitoring for Snowflake performance, consumption and cost.
• Recommend architectural improvements to improve scalability while controlling cloud expenditure.

Integration & Analytics

• Integrate Snowflake with financial applications, APIs, databases, files and cloud services.
• Support BI and analytics platforms such as Power BI and Tableau.
• Develop curated datasets and semantic/data consumption layers for financial reporting.
• Support APIs and downstream applications consuming Snowflake data.
• Collaborate with application, data, cloud, security and business teams on end-to-end solutions.

Security, Governance & Production Support

• Implement Snowflake RBAC, least-privilege access, masking policies, row-level security and secure data sharing.
• Ensure appropriate protection of confidential financial and investor information.
• Support data lineage, governance, auditability and regulatory requirements.
• Lead troubleshooting of production incidents involving Snowflake, pipelines, integrations and data-quality issues.
• Conduct root-cause analysis and implement permanent corrective actions.

Required Technical Skills

• 8–12+ years of overall experience in database, data engineering, BI or enterprise data platforms.
• Minimum 4+ years of strong hands-on Snowflake experience.
• Expert-level SQL skills including complex queries, performance tuning and troubleshooting.
• Strong experience with Snowflake architecture, data modeling and workload optimization.
• Strong ETL/ELT and enterprise data-integration experience.
• Experience with Python or equivalent scripting/programming technologies for data engineering.
• Experience implementing automated data-quality and reconciliation controls.
• Experience with cloud platforms such as Azure or AWS.
• Experience with CI/CD, Git and automated deployment practices for data platforms.
• Strong understanding of data security, governance and access-control principles.

Mandatory Financial Application Experience

• Direct hands-on Investran experience is mandatory.
• Strong understanding of Investran database/data structures, financial data and integration patterns.
• Experience extracting, transforming, reconciling or reporting Investran data.
• Strong understanding of Private Equity / Alternative Investments / Investment Management data.
• Understanding of financial concepts including funds, investors, investments, commitments, capital calls, distributions, valuations and transactions.
• Ability to communicate effectively with both financial-domain stakeholders and technical engineering teams.

Preferred Skills

• Experience with other investment-management or financial platforms in addition to Investran.
• Experience with Snowflake + Power BI enterprise architectures.
• Exposure to Azure Data Factory, Databricks, dbt or equivalent modern data-engineering technologies.
• Snowflake certification such as SnowPro Core or Advanced.
• Experience with financial data warehouses, regulatory reporting or investor reporting.
• Exposure to data governance, metadata management and lineage platforms.
• Experience modernizing legacy financial data platforms into Snowflake.

AI-First Engineering

• Ability to leverage AI-assisted development tools to accelerate SQL development, data mapping, documentation, testing and troubleshooting.
• Exposure to LLM-assisted data engineering and intelligent automation is desirable.
• Ability to identify opportunities for AI-assisted financial-data reconciliation, anomaly detection and operational automation while maintaining appropriate security and governance controls.

Ideal Candidate Profile

The successful candidate will be a hands-on technical leader rather than a coordination-only lead. The individual should be capable of independently understanding an Investran data problem, analyzing its source structures, designing the appropriate Snowflake architecture, developing or reviewing the implementation, troubleshooting issues and explaining the resulting financial data to business stakeholders.

Critical Screening Criteria

MUST HAVE: Snowflake + Advanced SQL + Data Engineering/ETL + Direct Investran Experience + Financial Services/Private Equity domain knowledge.

NO-GO: Candidates with strong Snowflake skills but no direct hands-on Investran experience should not progress to the technical interview.