Role: Informatica IDMC

Location: Pan India

Experience: 7 to 14 years

Immediate Joiners Only

Platform: Informatica Intelligent Data Management Cloud (IDMC), including Cloud Data Quality and Data Profiling

Role Type: Full-time assignment / contract

Experience Level: Mid–Senior

Role Purpose The Informatica IDMC Data Quality Engineer will implement, test, deploy, and stabilize data quality capabilities for prioritized business data across multiple rollout waves. The role converts approved Critical Data Elements, business rules, thresholds, and pain points into reusable technical DQ rules, profiling assets, scorecards, exception processes, and production-ready monitoring within IDMC. The engineer will work closely with functional DG/DQ leads, the IDMC Technical Lead, source-system SMEs, data catalog specialists, data engineers, business data owners, and stewards.

Key Responsibilities

- Review approved CDE registers, DQ rule catalogs, business definitions, thresholds, pain points, and source-to-field mappings for each rollout wave.
- Profile SAP-originated and other prioritized datasets landed in Azure Data Lake Storage, and analyze completeness, validity, uniqueness, consistency, accuracy, and integrity issues.
- Design and configure reusable Informatica Cloud Data Quality assets, including rule specifications, mappings, dictionaries/reference data, scorecards, validation controls, and monitoring jobs.
- Implement technical DQ rules mapped to CDEs and business rules; configure thresholds, pass/fail logic, alerts, exception capture, and rerun processes.
- Establish exception handling and support remediation workflows for failed records, including traceability to source data and responsible data owners or stewards.
- Support integration of DQ rules and results with IDMC catalog, workflows, metadata, lineage, monitoring dashboards, data marketplace, and enterprise data portal capabilities.
- Validate source connectivity, profiling, metadata harvesting, and data movement between the enterprise data lake or landing area, IDMC, and the DQ repository.
- Work with SAP SMEs to validate SAP tables, fields, extraction logic, cross-table relationships, landed file structures, and interpretation of rule failures.
- Create functional and non-functional test plans, test cases, test data, execution evidence, defect logs, and traceability matrices.
- Perform unit, system, integration, regression, UAT, and production validation; diagnose defects and tune rules, jobs, and thresholds based on stakeholder feedback.
- Support deployment across development, QA, UAT, and production environments, including release packaging, configuration migration, change records, go-live checks,



and rollback readiness.
- Monitor job runs and DQ scores, troubleshoot failures, optimize performance, and provide hypercare and production stabilization support.
- Maintain technical design details, configuration inventories, rule documentation, deployment records, operating procedures, runbooks, and knowledge-transfer materials.
- Participate in sprint planning, estimation, daily delivery ceremonies, demos, status reporting, risk/dependency management, and sign-off preparation.

Primary Deliverables
- Configured profiling tasks and documented profiling results for prioritized tables.
- Production-ready DQ rule libraries for rollout waves
- Implemented validation, alerts, scorecards, exception handling, and rerun processes.
- Validated IDMC connectivity and integration with the enterprise data lake and DQ repository.
- DQ outputs integrated with catalog, lineage, workflows, dashboards, marketplace, and portal capabilities as applicable.
- Deployment packages and evidence supporting QA, enterprise architecture, security, compliance, CAB, and production approvals.
- Technical configuration documentation, operating procedures, runbooks, and knowledge-transfer materials.
- Hypercare support, issue resolution, and stabilization outputs following each release.

Required Technical Skills
- Robust hands-on experience with Informatica IDMC, especially Cloud Data Quality and Cloud Data Profiling.
- Practical experience building rule specifications, mappings, dictionaries, reusable DQ assets, profiling tasks, scorecards, exception management, and monitoring processes.
- Strong understanding of data quality dimensions, rule design, thresholds, controls, root-cause analysis, remediation, and data-quality KPIs.
- Proficiency in SQL for profiling, reconciliation, source validation, test-data preparation, and defect analysis.
- Experience with cloud data lakes and file-based ingestion, preferably Azure Data Lake Storage Gen2, CSV-based landing patterns, and enterprise data pipelines.
- Experience integrating IDMC with relational databases, ERP systems, cloud platforms, catalog/metadata capabilities, and downstream reporting solutions.
- Working knowledge of data governance concepts, including CDEs, business glossaries, stewardship, technical metadata, lineage, policy linkage, workflows,



and data marketplaces.
- Ability to create clear technical specifications, rule inventories, traceability documents, test evidence, deployment guides, and runbooks.

Preferred Experience
- 5+ years of overall data management, data engineering, or data quality experience, including at least 2–3 years of hands-on Informatica cloud data quality implementation.
- Experience delivering enterprise DG/DQ programs through phased or sprint-based rollouts.
- Experience working with SAP source data or SAP-to-data-lake integration patterns.
- Exposure to Informatica Cloud Data Governance and Catalog, metadata scanning, lineage, workflow, and marketplace capabilities.
- Experience supporting Power BI or other DQ monitoring dashboards.
- Experience in energy, petrochemicals, manufacturing, or similarly complex regulated environments is an advantage.

Good to have skills
- Informatica Cloud Data Quality Professional certification or equivalent IDMC training is desirable.

Education and Professional Competencies

- Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Management, or a related discipline.
- Strong analytical and problem-solving skills, with the ability to distinguish genuine DQ defects from expected source-system behavior.
- Effective communication with business stakeholders, data owners, stewards, architects, platform administrators, and engineering teams.
- Ability to work independently, manage assigned backlog items, estimate accurately, and deliver within fixed sprint and release timelines.
- Strong attention to documentation quality, auditability, evidence management, and stakeholder sign-off requirements.

Key Interfaces The engineer will report operationally to the IDMC Technical Lead and collaborate closely with the Data Governance and Quality functional team, source-system SMEs, data catalog and metadata specialists, the IDMC platform administrator, data engineers, dashboard developers, enterprise IT teams, and business owners and stewards across the in-scope domains.

Success Measures

- DQ rules are delivered for assigned tables and CDEs within committed sprint timelines.
- Rules, thresholds, alerts, and exception processes are traceable to approved business requirements.
- Profiling and rule results are accurate, reproducible, and accepted by business owners and stewards.
- Test evidence and deployment documentation meet project governance, security, compliance, and CAB expectations.
- Production jobs are stable, monitored, performant, and supported through hypercare.
- Technical knowledge is successfully transferred to the designated support and data teams

📌 Informatica IDMC -Cloud Data Quality (CDQ) (Bengaluru)
🏢 Innova ESI
📍 Bengaluru