Striim Data Architect – Data Quality & Governance

Job Description

• Design robust data architecture blueprints that integrate Striim data quality, Striim data governance, and Striim data integration capabilities to deliver highly reliable and compliant enterprise data flows that support critical analytical and operational workloads across the organization. 
• Lead the end to end definition of data quality frameworks using DQG architect patterns and Striim data quality modules to enforce standardized validation rules, anomaly detection processes, and consistent remediation workflows across diverse data domains.
• Oversee the translation of complex business requirements into scalable technical solutions using Python and PL SQL so that data pipelines, transformations, and validations are implemented in a maintainable and performance optimized manner.
• Provide technical guidance on the use of TQL for streaming data processing and rule configuration to enable near real time monitoring of data quality indicators and proactive resolution of issues before they impact downstream consumers.
• Drive optimization of RDBMS and SQL based data stores by reviewing schema designs, indexing strategies, and query structures to ensure that data quality checks and integration workloads run efficiently and support future growth in data volume and complexity.
• Collaborate closely with product owners, data stewards, and engineering teams to prioritize data quality and governance initiatives that directly enhance business reporting accuracy, regulatory adherence, and stakeholder trust in shared information assets.
• Implement reusable patterns and reference solutions for hybrid work teams that standardize how Python scripts, PL SQL procedures, and Striim components are orchestrated, versioned, and monitored to maintain consistency across different projects and regions.
• Guide end users and technical teams on best practices for data profiling, metadata management, and lineage capture within Striim platforms so that the organization gains transparent visibility into the origin, transformation, and usage of critical data sets.
• Coordinate structured reviews of data integration pipelines to identify sources of data drift, incomplete records, or inconsistent transformations and then define targeted remediation steps that improve overall data reliability and reduce operational rework.
• Develop detailed documentation and architectural diagrams that describe current and target state data landscapes, highlighting how data governance controls and quality checks are embedded in each integration layer to meet internal and external requirements.
• Partner with security and compliance stakeholders to align data governance configurations, access policies, and retention rules with corporate standards, ensuring that sensitive data is handled responsibly and supports broader societal expectations for privacy.
• Mentor team members in applying sound architectural judgment when building Python based utilities, PL SQL routines, and Striim flows so that solutions remain resilient, testable, and adaptable to evolving business strategies and technology platforms.
• Analyze production incidents related to data integrity or pipeline failures by tracing issues across Python code, PL SQL logic, and Striim configurations and then propose targeted improvements that prevent recurrence and strengthen system robustness

Thanks and Regards!

Lavkesh Dwivedi

 
Amtex System Inc.

7 World Trade Center, 46th Fl, Suite 4625

250 Greenwich Street

New York, NY 10007