Aashish Prashant — Software Engineer

I am a Data Quality Engineer and Data Governance Specialist with 7+ years of experience building enterprise-grade data quality, metadata, and integration solutions across cloud and modern data platforms. My expertise is centered around Informatica IDMC—including Cloud Data Quality (CDQ), Cloud Data Integration (CDI), Cloud Data Governance & Catalog (CDGC), Mass Ingestion/MCC, and CLAIRE AI—where I design scalable, metadata-driven frameworks that improve trust, visibility, and automation across the data lifecycle. I specialize in creating reusable, parameterized, multi-tenant Data Quality frameworks that eliminate hardcoding and allow self-service DQ rule configuration for both technical and business teams. My work includes building DQ rule repositories, profiling pipelines, lineage-aligned rule execution, scorecards, exception handling, and DQ observability dashboards. I also integrate DQ and governance capabilities seamlessly into ETL/ELT pipelines using Databricks (PySpark, Delta Lake), Python, SQL, and AWS services. On the governance side, I have strong experience in metadata management, business glossary curation, automated term assignment (CLAIRE), data classification, cataloging, and stewardship workflows. I frequently work on connecting technical metadata from cataloged assets to curated business terms, enabling true end-to-end lineage and business context. My engineering background includes ETL development, taskflow automation, API integration, orchestration using Databricks Workflows/TIDAL/Airflow, schema evolution handling, and data warehousing concepts. I use Python extensively for automating IDMC taskflows, monitoring runs, generating payloads, and implementing logic-driven DQ execution models. I’m passionate about enabling organizations to treat data as a strategic, governed, high-quality asset—ensuring it is trusted, compliant, discoverable, and ready for decision-making. My goal is always to simplify adoption, strengthen governance, and bring data quality closer to both business and engineering users.

Stackforce AI infers this person is a Data Quality and Governance Specialist in the SaaS industry.

Location: Bengaluru, Karnataka, India

Experience: 6 yrs 6 mos

Skills

Data Governance
Data Quality

Career Highlights

Expert in building scalable data quality frameworks.
Strong experience in data governance and metadata management.
Passionate about treating data as a strategic asset.

Work Experience

Virtusa

Lead Software Engineer (4 mos)

Takeda

Data Quality (1 yr 9 mos)

Deloitte

Consultant (10 mos)

Buisness Technology Analyst (2 yrs 2 mos)

Capgemini

Associate Consultant (5 mos)

Senior Analyst (1 yr)

Analyst (1 yr 2 mos)

Education

Bachelor of Engineering - BE at Lakshmi Narain College of Technology, Kalchuri Nagar, Raisen Road, Post Klua, Bhopal-462021

Aashish Prashant

Software Engineer

Bengaluru, Karnataka, India6 yrs 6 mos experience

Highly Stable

Key Highlights

Expert in building scalable data quality frameworks.
Strong experience in data governance and metadata management.
Passionate about treating data as a strategic asset.

Stackforce AI infers this person is a Data Quality and Governance Specialist in the SaaS industry.

Contact

Skills

Core Skills

Data GovernanceData Quality

Other Skills

Metadata ManagementDatabricksHardware SizingData ProfilingPL/SQLInformatica PowerCenterExtract, Transform, Load (ETL)MySQLAgile MethodologiesScaled Agile FrameworkPySparkDatabricks ProductsPython (Programming Language)Tidal Enterprise SchedulerREST APIs

About

Experience

6 yrs 6 mos

Total Experience

2 yrs

Average Tenure

4 mos

Current Experience

Virtusa

Lead Software Engineer

Jan 2026 – Present · 4 mos · Bengaluru, Karnataka, India · On-site

I work on Enterprise Data Quality Framework that enables the organization to operationalize data quality across multiple business domains and critical data assets.
The framework is designed using a metadata-driven architecture, where data quality rules, rule templates, dataset mappings, and thresholds are centrally governed in a metadata repository rather than embedded within individual pipelines. This approach allows us to standardize rule management and scale data quality monitoring across hundreds of datasets in a consistent way.
One of the key capabilities of the platform is a centralized rule library built on reusable rule templates aligned to core data quality dimensions such as completeness, validity, uniqueness, referential integrity, and format conformity. Instead of building rules individually for every dataset, these templates are parameterized, allowing governance teams to dynamically apply the same rule logic across multiple datasets simply by configuring metadata parameters.
To support enterprise governance, the framework also enables self-service rule onboarding for data stewards and domain owners, where they can configure and manage data quality rules through a governed interface without requiring engineering intervention. As part of this process, the platform performs automated rule validation and governance checks before rules are deployed, ensuring that rule logic, dataset references, and configurations meet governance standards.
Once approved, rules are executed through the data processing layer and the results are captured in a centralized data quality metrics and scorecard repository, which tracks rule performance, failure patterns, and historical data quality trends.
These insights are then surfaced through data quality scorecards and executive dashboards, giving data owners, stewards, and governance councils clear visibility into the health of their data assets and helping them proactively identify and remediate quality issues.

Data GovernanceMetadata ManagementData Quality

Takeda

Data Quality

Apr 2024 – Jan 2026 · 1 yr 9 mos · Bengaluru, Karnataka, India · Hybrid

As a Data Quality Developer, I focus on enhancing and optimizing the Enterprise Data Quality (DQ) Framework to ensure data integrity, consistency, and accessibility across multiple business units. I work on designing and implementing scalable and automated DQ solutions that empower business users, enabling them to take ownership of data quality and governance.
A key part of my role is enabling a user-friendly UI that allows business users to easily define, monitor, and manage data quality rules without deep technical expertise. By making DQ more accessible and self-service-driven, I help foster a stronger data governance culture across the organization.

Data QualityDatabricksData Governance