Database Architecture & Data Engineering Services
Database Optimization - Data Pipelines - Cloud Migrations
Upgrades - High Availability - Disaster Recovery
Data Platforms & Architecture
Design and architecture of data platforms supporting analytics, reporting, and downstream systems. Services include structuring data for efficient querying, organizing storage in Snowflake, Amazon Redshift, and other AWS-based data warehousing environments, and designing scalable data flows to accommodate increasing data volumes and complexity.
Data Quality & Validation
My work in data quality focuses on identifying and resolving issues across pipelines and source systems.
This includes implementing validation logic, detecting anomalies, and enforcing data consistency before data reaches downstream systems and reporting layers.
Data quality metrics are captured and surfaced through dashboards, providing visibility into data integrity over time. Large-scale assessments are performed using PySpark to analyze and validate high-volume datasets efficiently.
Experience supporting data governance initiatives, including master data management, data standardization, and validation workflows using platforms such as Semarchy xDM.
Work in this area focuses on improving data consistency, establishing clear data definitions, and supporting processes that maintain data quality across systems.
Data Governance & Master Data Management
Data Integration & Pipeline Development
Development of data processing pipelines using Python, PySpark, AWS Glue, and dbt. Workflow orchestration using AWS Glue and Apache Airflow. Integration with a variety of data sources, including Amazon S3, Redshift, Snowflake, REST APIs, and major relational database platforms.
Database Systems & Performance
I provide support for underlying database systems, including installation and configuration, upgrades, performance tuning, query optimization, and high availability design for MySQL, PostgreSQL, and related platforms.
Monitoring & Observability
Implementation of monitoring and alerting for data pipelines and database systems using tools such as AWS CloudWatch, Performance Insights, and Percona Monitoring & Management.
Monitoring is designed to provide visibility into pipeline execution, system performance, and data quality, including notifications when measured metrics exceed defined thresholds.