Project Type: B2B Resource
Project Timeline: May 2025 – July 2025
Client Region: APAC
Industry: Finance

Context & Objectives

The client operated a large-scale data platform with multiple ingestion and transformation layers (Bronze, Silver, and Gold) supporting several business domains. Each program maintained its own reconciliation processes, often manual and inconsistent, leading to significant QA overhead and delays in production sign-offs. Analysts frequently encountered mismatched numbers across layers, causing escalations and eroding trust in analytics outputs.

The objective was to build a Reconciliation Accelerator, a unified, automated framework to validate data parity, completeness, and domain-specific business rules across pipeline layers.
This accelerator aimed to standardize quality checks (row counts, aggregates, null detection, and custom logic), generate variance dashboards, and trigger alerts for anomalies. By automating reconciliation, we sought to reduce manual QA time, accelerate sign-offs, and improve confidence in data reliability.

Project Goals

The key goals of the Reconciliation Accelerator project were centered around building trust, consistency, and automation in data validation processes across the Bronze, Silver, and Gold layers of the lakehouse. We wanted to ensure that data parity and quality checks became systematic, repeatable, and governed, rather than manual and reactive.

• Standardize Reconciliation Checks
  The primary goal was to establish a unified and configurable library for reconciliation checks, covering row counts, aggregates, null validations, and domain-specific rules. This standardization was intended to make validation logic consistent across all data layers and domains.

• Automate Detection and Reporting
  Another key goal was to eliminate manual QA cycles by automating the execution of checks and surfacing results through dashboards and alerts. We wanted variances and anomalies to be detected early and reported with clear severity levels for faster resolution.

• Improve QA Efficiency and Data Trust
  We aimed to reduce weeks of manual data validation into hours of automated checks, allowing teams to focus on insights rather than troubleshooting. The goal was to enhance confidence in analytical outputs by ensuring data consistency and accuracy at every stage.

Challenges

Implementing an enterprise-scale reconciliation framework involved several technical and operational complexities. The client’s environment included hundreds of datasets, each with its own business rules, varying update frequencies, and ownership models, making consistency and reliability difficult to achieve.

1. Manual & Inconsistent QA
   Reconciliation processes were primarily manual, with teams maintaining individual SQL scripts or Excel trackers. This led to inconsistencies, delays, and errors, and there was no single source of truth to ensure alignment across domains.

2. Scalability & Performance
   The platform needed to handle thousands of tables and checks across Bronze, Silver, and Gold layers daily. Balancing execution efficiency, batch scheduling, and parallelism without exceeding compute budgets was a major challenge.

3. Governance & Ownership
   Ensuring proper access control and ownership while masking sensitive data (PII) in logs and dashboards was critical. Domain-specific ownership had to be enforced while maintaining compliance and audit readiness.

4. Reliability & Cost Management
   The system had to provide accurate and complete checks while being cost-efficient. Full validations were needed for critical datasets, whereas heavy workloads required intelligent sampling and selective execution to avoid unnecessary resource consumption.

Solution Overview

To overcome the challenges of manual, inconsistent, and resource-intensive reconciliation, we designed and implemented the Reconciliation Accelerator, a scalable and automated framework for parity checks and domain-specific validations across Bronze, Silver, and Gold layers. The solution focused on operational reliability, governance, and cost-efficient performance.

1. Config-Driven Reconciliation
   We developed configurable templates that allowed teams to define reconciliation checks clearly and consistently. Each configuration captured source and target tables, join keys, filters, thresholds, check types (row, sum, null, or custom), and owner mappings.

2. Automated Execution Engine
   A central execution engine parsed these configurations and ran validations automatically. It supported scheduled, ad-hoc, and selective runs by table or domain. Failures triggered automatic alerts and generated JIRA or ServiceNow tickets for owners to investigate.

3. Variance Dashboards & Alerts
   Validation results were stored in Unity Catalog tables with metadata including pass/fail status, variance percentages, run duration, and severity levels. Power BI dashboards displayed trends, failure heatmaps, and freshness indicators, while automated alerts highlighted high-severity deviations.

4. Governance & Auditability
   We enforced least-privilege access, masked sensitive data in logs and dashboards, and maintained detailed audit trails for every reconciliation run. Temporary exemptions with expiry dates allowed flexibility while keeping changes fully traceable.

5. Operational Efficiency & Reliability
   The system leveraged parallelized execution, SLO-aware scheduling, and batch windows to balance scale, speed, and cost. Sampling logic was applied to heavier workloads, reducing compute usage while maintaining robust coverage.

By implementing the Reconciliation Accelerator, we transformed a largely manual and error-prone process into an automated, reliable, and governed workflow. Teams could now quickly detect data drift, ensure parity across layers, and enforce domain-specific rules, all while maintaining operational efficiency, audit readiness, and trust in the data.

Data Model & Semantics

The Reconciliation Accelerator framework formalized both the specification and results of reconciliation checks.
Each check specification defined critical attributes, including source and target tables to compare (e.g., Bronze→Silver), join keys and filters for selecting relevant data subsets, the type of check to perform (row count, aggregate, null, threshold, or domain-specific rules), acceptable variance thresholds, and the responsible owner or domain team for sign-off.
On the results side, the framework captured each run’s outcome, including pass/fail status, variance percentage and severity, execution duration, SLA compliance, and relevant audit metadata.
This structured model ensured that checks were consistent, traceable, and actionable while providing full visibility into the integrity and reliability of the data across layers.

Ops, Security, Quality & Performance

The Reconciliation Accelerator was designed to operate efficiently, securely, and reliably at scale.

• On the operations side, the framework supported scheduled runs as well as selective or ad-hoc executions, with automatic ticket creation for any failures. Detailed runbooks provided guidance for operations teams to investigate issues, apply fixes, and recover from errors quickly.

• From a security perspective, least-privilege roles ensured that only authorized users could access sensitive data, while logs and dashboards masked PII and other confidential information. Owner-based permissions maintained accountability, ensuring that each domain team was responsible for its checks.

• Quality was enforced through curated check packs for each domain, with calibrated thresholds and standardized validations to maintain consistency across datasets. Expiry-based exemptions allowed temporary skips of certain checks without compromising long-term data integrity.

To optimize performance and cost, heavy checks were executed using sampling techniques, and batch executions were staggered across time windows to avoid overloading compute resources. Safe parallelism was applied wherever possible to accelerate execution while keeping operational costs under control, ensuring that large-scale reconciliation remained efficient and sustainable.

Tech Stack

Data Sources:

• Config sets defining reconciliation checks and corresponding source/target tables across data layers.

Ingestion:

• Config-driven controller supporting domain or table-level selective runs.

Storage / Lakehouse:

• Unity Catalog tables storing check results, variance percentages, and run metadata.

Orchestration:

• Scheduled or on-demand batch runs with automatic ticket generation on failure.

Transformation / Modeling:

• Bronze>Silver>Gold parity comparisons and domain-specific rules with thresholds.

Serving / Consumption:

• Power BI dashboards showing variances, freshness indicators, and alerts via email/ops channels.

Governance / Security:

• Owner mappings, exemption windows, and compliance exports for regulated reporting.

Observability / Quality:

• Failure heatmaps, trend analysis, MTTR tracking, and monthly variance reports.

DevOps / CI/CD:

• Version-controlled check catalogs, review cadence, and packaged defaults per domain.

FinOps / Cost Management:

• Batched and sampled checks, SLO-aware scheduling, and parallelization toggles.

Outcomes & Business Impact

The Reconciliation Accelerator significantly improved the way data quality and parity checks were handled across the organization. Manual reconciliation, which used to take weeks, was reduced to just a few hours, allowing teams to sign off on data faster and with greater confidence.
Standardized check libraries ensured that KPIs and metrics were consistent and comparable across different domains. Dashboards provided clear visibility into failures, trends, and ownership, making it easier to track issues and follow up with the right teams.
By automating the process, the organization reduced QA effort, minimized repeated “numbers don’t match” escalations, and improved overall operational reliability. Early detection of discrepancies through alerts and runbooks shortened recovery time, while governance rules and temporary exemptions ensured compliance without slowing down workflows.

Deliverables

Check Catalog Templates & Domain Packs: Predefined configurations for standard and domain-specific checks.

Alerting & Dashboards: Variance visualizations with severity-based alerting and freshness indicators.

Owner Workflow: Sign-off and exemption workflow for data owners and QA teams.

Ops Runbooks & Trend Reports: Operational guidance and monthly variance trend reporting.

Conclusion: Enabling Automated Trust in Data Quality

The Reconciliation Accelerator created a robust system for automated, scalable, and governed data quality management across the organization. By standardizing reconciliation checks and integrating them into daily operations, the framework ensured early detection of data drift, quicker issue resolution, and consistent, reliable metrics across domains.
Business users gained confidence that the numbers they were using for decisions were accurate and trustworthy, while QA teams no longer needed to spend weeks manually validating data, significantly reducing their workload. Leadership and stakeholders benefited from real-time visibility into data health, trends, and variances, allowing them to make informed, timely decisions.

At the same time, the framework maintained strict governance and auditability, with clear ownership, exemption controls, and compliance reporting. Cost efficiency was preserved through selective checks, batch execution, and parallelization, ensuring that data quality assurance did not compromise operational budgets. In short, the solution delivered reliable insights, operational resilience, and trust in the organization’s data at scale.