Build data pipelines that just work

Reliable AWS data infrastructure from ingestion to transformation to storage. Pipelines built with monitoring, error handling, and scalability from day one.

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ETL pipelines that fail frequently and require manual intervention

Data quality issues that make analytics unreliable

Need to build or scale data infrastructure but lack in-house expertise

Processing times are too slow for business needs

ETL Pipeline Development

Design and build data pipelines using AWS Glue, Lambda, and Step Functions with proper error handling and retry logic.

Data Lake Architecture

S3-based data lakes with proper partitioning, cataloging (Glue Catalog), and query optimization for Athena.

Data Warehouse Design

Redshift and RDS data warehouse schemas optimized for your analytics and reporting needs.

Data Quality & Monitoring

Automated data validation, quality checks, alerting, and dashboards so you know when something's wrong.

AWS Services

AWS Glue S3 Redshift Lambda Step Functions Athena Kinesis RDS

Related Case Study

Custom Analytics Platform Replacing $200K COTS Solution

Built a custom analytics platform querying 144M+ records across 40 years of data. Replaced an expensive COTS solution while bridging legacy and modern systems with sub-second performance.

$200K

Annual savings

Read the full case study →

What AWS data services do you work with?

I work extensively with AWS Glue (ETL jobs, crawlers, Data Catalog), S3 (data lakes), Redshift (data warehousing), Lambda, Step Functions (orchestration), Athena (queries), Kinesis (streaming), and related services like CloudWatch for monitoring and SNS for alerts.

Can you work with our existing data infrastructure?

Yes. I regularly integrate with existing systems—whether that's connecting to legacy databases, third-party APIs, or working within an established AWS environment. I can assess your current infrastructure and recommend incremental improvements rather than requiring a complete rebuild.

How do you handle data quality and monitoring?

Data quality checks are built into every pipeline—validating schemas, checking for nulls/duplicates, verifying row counts, and more. I implement CloudWatch dashboards and alerts so you're notified immediately when something fails. The goal is pipelines that run reliably without constant babysitting.