Job Requirements

QA Lead - Data Pipeline & Reconciliation

Client Portland General Electric (PGE)

Project Enterprise Data Lake Modernization - Oracle CTM/ADW to Kraken SaaS Migration

LocationPune

Experience10 years in data/ETL/DWH testing and migration validation

Work alignment Availability for client calls aligned to agreed PGE/PST overlap

Role Summary The QA Lead owns end-to-end quality strategy, data validation, parallel-run reconciliation, defect governance, and downstream data certification for the Oracle CTM/ADW to Kraken migration on Snowflake and dbt. The role leads coexistence testing across approximately 20 enterprise data products and 30 outbound file/SFTP exports and leads the large-scale Oracle-versus-Kraken comparison for approximately 190,000 accounts in Phase 3.

Key Responsibilities

· Define and own the master test strategy, test plan, entry/exit criteria, traceability, test data approach, reconciliation scenarios, regression scope, and sign-off evidence across all project phases.

· Review source-to-target mappings, Data Contracts, business rules, identifier cross-reference logic, and record-key strategy; translate them into functional, data, schema, integration, regression, and negative test scenarios.

· Lead Oracle-versus-Kraken parallel-run validation across Snowflake Bronze, Silver, Gold, coexistence tables, control tables, and consumer-facing semantic views.

· Validate row counts, column values, schema/data types, nulls, duplicates, referential integrity, usage totals, billing balances, transformation rules, and aggregate accuracy.

· Define tests for Kraken inserts, updates, and deletes; confirm append-only Bronze history and latest-active-record/overwrite behavior in Silver and Gold.

· Validate cohort/customer routing through central control and mapping tables; ensure consumer views return the correct C2M or Kraken records without duplication,



omission, or cross-source mixing.

· Validate dbt model outputs against approved mappings and legacy Snowflake logic, including schema tests, custom data tests, business-rule assertions, and 15-minute meter-reading aggregations where applicable.

· Lead automated reconciliation and discrepancy-alerting implementation or adoption using SQL, dbt, and/or Python; define tolerances, exception handling, rerun controls, and evidence retention.

· Coordinate end-to-end data certification for approximately 20 downstream data products and 30 file/SFTP exports, validating data contracts, completeness, schema compatibility, and functional outputs.

· Lead defect triage, severity/priority assessment, root-cause collaboration, retesting, regression impact analysis, quality dashboards, and risk/escalation reporting.

· Lead QA support for SIT, UAT, Production Readiness Review, go-live, sign-off, and hypercare; ensure test evidence and reconciliation reports are audit-ready.

· Lead Phase 3 large-volume dual-run reconciliation for approximately 190,000 accounts and report unresolved variances, business impact, and release recommendation.

· Guide QA Testers, review test cases and SQL, establish testing standards, and partner with Data Engineering, Architecture, business owners, and reporting teams.

Required Skills & Experience

· 10 years of experience in data testing, ETL/DWH testing, data migration testing, or quality engineering for enterprise data platforms, including lead ownership.





· Advanced SQL for complex source-to-target reconciliation, joins, set comparisons, aggregate balancing, duplicate/null analysis, and high-volume exception investigation.

· Strong hands-on Snowflake data-validation experience across layered/medallion architectures and large-scale datasets.

· Strong knowledge of dbt testing, model validation, business-rule assertions, lineage/traceability, and CI/CD-integrated quality controls.

· Proven experience in parallel-run or dual-stream reconciliation for legacy-to-up-to-date platform migrations.

· Experience defining or implementing automated reconciliation frameworks using SQL, dbt, and/or Python, including discrepancy alerts and repeatable evidence.

· Experience validating append-only/CDC-style ingestion, source-to-target mappings, Data Contracts, control-table routing, semantic views, and outbound files.

· Strong defect-management, test-governance, stakeholder communication, UAT/PRR coordination, and release-readiness skills.

Preferred Skills

· Utilities/energy or customer-to-meter domain experience, including AMI interval, billing, customer, account, and meter data.

· Experience validating Tableau or other BI consumers and SFTP/file-based interfaces.

· Exposure to Databricks-to-Snowflake ingestion patterns, Snowflake Dynamic Tables, and table-based coexistence architectures.

· Experience in regulated, SLA-bound, or audit-controlled delivery environments.

Qualifications

· Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; equivalent relevant experience may be considered.

Role Boundary The role validates data delivered to downstream consumers and supports downstream certification. Changes to, and testing of, downstream application code are outside this role scope.

📌 QA Lead - Data Pipeline & Reconciliation (Pune)
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