97 ) Metadata Management Solution Architect -banking project

 

Metadata Management Solution Architect

1. Definition, Role, and Responsibilities of a Metadata Management Solution Architect

Definition

A Metadata Management Solution Architect is a specialized technical leader who designs enterprise data governance frameworks, catalogs, and lineage systems. They ensure data discoverability, quality, compliance, and traceability across multi-cloud and on-premises ecosystems.

Core Roles & Responsibilities

• Catalog Architecture: Designing enterprise metadata platforms (e.g., AWS Glue Data Catalog, Azure Purview, Collibra) to index structured, semi-structured, and unstructured data assets.

• Data Lineage & Traceability: Mapping end-to-end data flow from source ingestion systems through transformation pipelines to consumption dashboards.

• Data Governance & Compliance: Enforcing policies, data ownership definitions, glossary standards, and regulatory compliance (e.g., GDPR, Basel III, PCI-DSS in banking).

• Automated Harvester Integration: Building automated metadata harvesting pipelines to capture technical, operational, and business metadata from databases, ETL tools, and BI platforms.

• Data Quality & Stewardship: Establishing frameworks for data quality monitoring, data dictionary maintenance, and role-based access to metadata assets.

2. Sample Project: Enterprise Metadata Management & Data Lineage for a Global Banking System

Existing System

• Infrastructure: A fragmented banking landscape with core banking databases (Oracle/SQL Server), multiple data warehouses (Snowflake/Teradata), and scattered ETL pipelines (Airflow, Informatica) across different geographic regions.

• Workload: Processes retail banking transactions, credit risk calculations, anti-money laundering (AML) checks, and financial reporting.

• Pain Points: Lack of a single source of truth for data definitions, unknown data lineage causing compliance blind spots for regulatory audits, and heavy reliance on tribal knowledge to track where critical risk metrics originate.
  
  

• Complete Guide: Implementing Master Data Management (MDM) on AWS for Banking

  Scenario: The Banking Customer Record Problem

  Simple English Explanation

  Imagine you walk into a bank, open a savings account, and the system records your name as "Robert Smith" with your home address. Six months later, you apply for a credit card through the bank’s mobile app, and because the app uses a completely different database system, you type your name as "Bob Smith" with your office address.

  When the bank's computers try to look you up, they treat Robert Smith and Bob Smith as two entirely separate people. Because your data is scattered and fragmented across separate banking silos (credit cards, loans, mobile apps, and core banking), the bank has no single, unified view of who you are.

  Problem Statement & Why MDM Is Required

  • Problem Statement: The bank cannot generate a single, unified view of its customers ("Golden Customer Record"), causing severe compliance risks and marketing inefficiencies during customer risk scoring audits.

  • Why it needs MDM and cannot be handled without it: Without an MDM system, basic SQL queries, standard ETL scripts, or data warehouses cannot automatically recognize that Robert Smith in the loan database and Bob Smith in the credit card database are the exact same human being. Standard data pipelines only move or copy data; they do not perform fuzzy matching, data deduplication, survivorship rules (deciding which phone number or address is the correct up-to-date one), or cross-system entity resolution. Without MDM, the bank risks severe regulatory penalties (such as anti-money laundering compliance failures) because it cannot accurately aggregate a customer's total financial exposure and risk score across all siloed systems.

  Full List of Tools Used for MDM on AWS

  To build, orchestrate, and manage this MDM solution on AWS, the following tool stack is utilized:

  1. AWS Database Migration Service (AWS DMS) / Airbyte: Used to stream and ingest raw customer records from various on-premises and cloud databases into AWS.

  2. Amazon S3: Acts as the raw landing zone and staging area for all incoming multi-source customer datasets.

  3. AWS Glue (Crawlers & Data Catalog): Scans the incoming datasets and maintains the technical schema metadata repository.

  4. AWS Glue ETL & Python (Custom Entity Resolution Engine): The core engine running fuzzy matching algorithms, standardization, and survivorship rules to merge records.

  5. Amazon SageMaker (Optional / Advanced): Used for advanced machine learning-based duplicate detection and probabilistic entity matching.

  6. Amazon RDS for PostgreSQL / Amazon Aurora: Stores the final "Golden Customer Records" and master data tables.

  7. Apache Airflow / AWS Step Functions: Orchestrates and tracks the entire MDM pipeline execution sequence from ingestion to golden record publication.

  Step-by-Step Implementation Guide

  Step 1: Ingestion of Fragmented Customer Data

  • What it means: Pulling raw customer records from isolated banking systems into a central cloud storage layer.

  • Tool Used: AWS DMS / Airbyte & Amazon S3

  • Technical Steps:

    • Configure source connectors to extract customer records from the core banking database, mobile app database, and credit card system.

    • Land the unedited JSON/CSV customer files into a secure staging bucket in Amazon S3:

      Plaintext

      s3://bank-mdm-landing-zone/raw_customers/
      

  Step 2: Schema Discovery and Metadata Cataloging

  • What it means: Scanning and indexing the structure of all incoming customer tables.

  • Tool Used: AWS Glue Data Catalog

  • Technical Steps:

    • Run an AWS Glue Crawler against the S3 landing zone to automatically inspect column formats (e.g., first_name, last_name, email, phone) and register them into the central metadata catalog so transformation jobs can access them.

  Step 3: Standardization, Matching, and Golden Record Creation (The Core MDM Step)

  • What it means: Running algorithms to clean typos, normalize phone numbers/addresses, match duplicate profiles, and merge them into a single "Golden Record."

  • Tool Used: AWS Glue ETL (Python/PySpark)

  • Technical Steps:

    • Write a PySpark script executed by AWS Glue that standardizes text fields (e.g., converting all names to uppercase, standardizing "St." to "Street").

    • Apply fuzzy matching algorithms (like Levenshtein distance or Jaro-Winkler) to catch misspellings between Robert Smith and Bob Smith.

    • Apply Survivorship Rules (e.g., if a customer has two different phone numbers, select the one with the most recent transaction timestamp) to build the definitive Golden Customer Record.

  Step 4: Loading and Storing the Golden Records

  • What it means: Saving the clean, unified master customer profiles into a secure master database.

  • Tool Used: Amazon RDS for PostgreSQL (Master DB)

  • Technical Steps:

    • Load the processed golden records from the Glue output path into the master database schema:

      SQL

      CREATE TABLE golden_customers (
          golden_customer_id VARCHAR(64) PRIMARY KEY,
          standardized_full_name VARCHAR(255),
          primary_email VARCHAR(255),
          primary_phone VARCHAR(50),
          combined_risk_score DECIMAL(5,2),
          last_updated_timestamp TIMESTAMP
      );
      

  Step 5: Orchestration and Workflow Tracking

  • What it means: Automating the entire MDM pipeline to run on a nightly schedule and tracking its execution health.

  • Tool Used: Apache Airflow / AWS Step Functions

  • Technical Steps:

    • Create a DAG workflow that sequentially triggers:

      1. Ingestion from source databases to S3.

      2. Glue Crawler schema updates.

      3. Glue ETL deduplication and matching script.

      4. Loading into the Golden Record PostgreSQL table, sending alert notifications via Slack or email if any step fails.

  .

3.  Interview Questions on the Banking Metadata Management Project

1. Architecture Choice: Why did you choose a modern active metadata platform (like Azure Purview or Collibra) over building a custom metadata database in PostgreSQL for a global bank?

2. Automated Lineage: How did you implement automated data lineage extraction from modern code-based transformation tools like dbt and orchestrators like Apache Airflow into the catalog?

3. Regulatory Compliance (Basel III / AML): How did this metadata management solution specifically solve the bank’s audit failure regarding Risk-Weighted Assets (RWA) lineage?

4. Technical vs. Business Metadata: How did you bridge the gap between technical metadata (column types, SQL queries) and business metadata (glossary terms, data owners) within the platform?

5. Sensitive Data Discovery: What automated classification mechanisms did you configure to detect and protect Personally Identifiable Information (PII) and financial records across cloud storage?

6. Handling Schema Drift: How does your metadata architecture handle downstream impact analysis and alerting when a core banking schema changes unexpectedly?

7. Integration with Legacy Systems: How did you harvest metadata from legacy on-premises databases (like Oracle or mainframe systems) that do not natively support modern cloud catalog APIs?

8. Data Stewardship & Governance Workflow: How did you enforce data ownership and approval workflows when business users updated data definitions in the glossary?

9. Performance & Scalability: In a massive banking environment with millions of assets, how did you optimize metadata scanning schedules to prevent performance degradation on operational databases?

10. ROI & Metrics: How did you measure the success and business value of the metadata management implementation (e.g., reduction in audit preparation time or data discovery speed)?