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, Energetic 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.

📌 Snowflake Techincal Lead - Financial Applications (Investran) (Bengaluru)
🏢 Knowledge Artisans
📍 Bengaluru