Build Scalable, Reliable Data Pipelines
I design and implement robust ETL/ELT pipelines orchestrated with Apache Airflow, data warehousing on Snowflake, and big data processing with PySpark and Hadoop. Whether you need to migrate data, process millions of records daily, or build real-time analytics pipelines, I deliver solutions that ensure data accuracy and reliability.
What Problems I Solve
Manual Data Processing
Automate repetitive data processing tasks with reliable pipelines that run on schedule and handle errors gracefully.
Data Quality Issues
Implement comprehensive data validation and quality checks that ensure 99.9%+ accuracy in your data pipelines.
Scaling Data Processing
Design pipelines that scale to handle growing data volumes from thousands to millions of records without performance degradation.
Complex Data Transformations
Handle complex schema transformations, data type conversions, and business logic across multiple heterogeneous data sources.
Real-Time vs Batch Processing
Design the right processing strategy for your use case, whether it's real-time streaming or efficient batch processing.
Data Warehouse Optimization
Optimize data warehouse performance and costs through efficient data modeling, partitioning, and query optimization.
Value I Deliver
99.9% Data Accuracy
Comprehensive validation frameworks ensure data quality and accuracy throughout the pipeline, catching errors before they impact downstream systems.
45%+ Performance Improvement
Optimize pipeline performance through parallel processing, efficient data transformations, and optimized data warehouse queries.
Automated Processing
Eliminate manual data processing work with automated pipelines that run on schedule, handle failures, and send alerts when issues occur.
Real-Time Data Availability
Enable faster decision-making with real-time or near-real-time data pipelines that make data available as soon as it's processed.
Scalable Architecture
Design pipelines that scale from thousands to millions of records without requiring major architectural changes.
Cost-Effective Solutions
Optimize data warehouse costs through efficient data modeling, partitioning strategies, and right-sized compute resources.
Real-World Implementations
Enterprise Data Migration ETL Pipeline
Challenge
Following OneTrust's acquisition of Convercent, we needed to migrate terabytes of sensitive compliance data across different database schemas. The challenge involved handling 50+ complex data types, maintaining 99.9% data accuracy, ensuring zero privacy violations, and completing the migration within tight deadlines for 20+ enterprise clients.
Solution
I designed and implemented an enterprise-grade ETL pipeline orchestrated with Apache Airflow:
- Apache Airflow DAGs for workflow orchestration and scheduling
- Flexible mapping engine handling schema transformations across 50+ data types
- Parallel batch processing architecture for performance optimization
- Multi-layer data validation at extraction, transformation, and loading stages
- Comprehensive error handling and retry mechanisms
- Data lineage tracking for audit and compliance requirements
- Monitoring and alerting for pipeline health and data quality issues
Results
The ETL pipeline successfully migrated 20+ enterprise clients with zero data loss incidents and zero privacy violations. Parallel batch processing reduced data processing time by 45% while maintaining strict data integrity requirements. The automated orchestration enabled reliable, repeatable migrations that accelerated time-to-value for the acquired customer base.
Technologies Used
Resilient Data Ingestion with Snowflake
Challenge
SecurityScorecard needed to ingest data from multiple heterogeneous sources (APIs, databases, files) into a centralized data warehouse. The challenge was to create resilient pipelines that could handle source failures, schema changes, and varying data volumes while ensuring data quality and timely availability for analytics.
Solution
I created resilient ingestion pipelines orchestrated with Apache Airflow, integrating with Snowflake for data warehousing:
- Apache Airflow DAGs for orchestrating multi-source data ingestion
- Snowflake data warehouse for scalable, performant data storage
- Resilient error handling with automatic retries and dead-letter queues
- Schema evolution handling for changing source data structures
- Incremental loading strategies to minimize processing time
- Data quality checks and validation before loading to Snowflake
- Monitoring dashboards for pipeline health and data freshness
Results
The pipelines successfully integrated data from all heterogeneous sources into Snowflake, providing a unified view for analytics. The resilient architecture handles source failures gracefully, and the Snowflake integration enables fast, scalable analytics queries. The system supports the platform's ability to handle 3x more vendors per customer.
Technologies Used
Big Data ML Pipeline Processing
Challenge
Serasa Experian, through Dextra Digital, needed to process 10M+ daily records for machine learning pipelines. The challenge was to build scalable data processing pipelines that could handle large volumes efficiently, support ML model training, and provide reliable data for analytics and decision-making.
Solution
I built PySpark/Hadoop ML pipelines for big data processing:
- PySpark for distributed data processing across large datasets
- Hadoop ecosystem for scalable storage and processing
- ML pipeline orchestration for feature engineering and model training
- Optimized Spark jobs for efficient resource utilization
- Data partitioning strategies for parallel processing
- Automated pipeline scheduling and monitoring
- Integration with ML frameworks for model training and inference
Results
The PySpark/Hadoop pipelines successfully process 10M+ daily records, providing clean, processed data for ML model training and analytics. The scalable architecture handles growing data volumes, and the ML pipeline integration enables automated feature engineering and model training workflows.
Technologies Used
Technologies & Tools I Work With
Orchestration
Data Warehouses
Big Data Processing
Databases
Cloud Storage
Data Quality
How I Work
Data Requirements Analysis
I analyze your data sources, understand data volumes, identify transformation requirements, and define data quality standards. This includes mapping source to target schemas and identifying business rules.
Pipeline Architecture Design
I design the pipeline architecture choosing between ETL and ELT patterns based on your needs. This includes selecting appropriate tools, designing data flow, and planning for scalability and reliability.
Data Quality Framework
I implement comprehensive data validation and quality checks at multiple stages of the pipeline. This includes schema validation, data type checks, business rule validation, and anomaly detection.
Orchestration & Scheduling
I set up workflow orchestration using Apache Airflow or similar tools, configure scheduling, implement error handling and retries, and set up dependencies between pipeline tasks.
Monitoring & Optimization
I implement monitoring and alerting for pipeline health, data quality issues, and performance metrics. I also optimize pipeline performance through tuning and optimization.
How We Can Work Together
End-to-End Pipeline Development
Complete pipeline development from design to deployment, including data quality frameworks and monitoring setup.
Pipeline Optimization
Optimize existing pipelines for performance, cost, and reliability. Refactor legacy pipelines to modern architectures.
Data Quality Audits
Assess data quality issues, implement validation frameworks, and improve data accuracy in existing pipelines.
Ongoing Pipeline Maintenance
Monthly retainer for ongoing pipeline maintenance, optimization, and support for your data engineering needs.
Why Choose Me
Petabyte-Scale Experience
Experience building and optimizing data pipelines that process petabytes of data across various industries and use cases.
Proven Accuracy
Consistent track record of achieving 99.9%+ data accuracy through comprehensive validation and quality frameworks.
Performance Optimization
Expertise in optimizing pipeline performance, achieving 45%+ improvements in processing time through efficient design.
Enterprise Experience
Proven experience with enterprise data pipeline requirements including compliance, security, and scalability needs.
Reliability Focus
Design pipelines with reliability as a core principle, including error handling, monitoring, and disaster recovery.
Maintainable Solutions
Build pipelines that are easy to understand, maintain, and extend, reducing long-term operational costs.
Frequently Asked Questions
How do you ensure data quality in pipelines?
I implement multi-layer data validation including schema validation, data type checks, business rule validation, and anomaly detection. Validation occurs at extraction, transformation, and loading stages. I use frameworks like Great Expectations and custom validators to catch data quality issues before they impact downstream systems. This approach has consistently achieved 99.9%+ data accuracy.
What's the difference between ETL and ELT?
ETL (Extract, Transform, Load) transforms data before loading into the data warehouse, while ELT (Extract, Load, Transform) loads raw data first and transforms it within the warehouse. I help choose the right approach based on your data warehouse capabilities, transformation complexity, and performance requirements. Modern cloud data warehouses like Snowflake excel at ELT patterns.
How do you handle data pipeline failures?
I implement comprehensive error handling including automatic retries with exponential backoff, dead-letter queues for failed records, checkpointing to resume from failures, and alerting for critical issues. Pipelines are designed to be idempotent, allowing safe retries without data duplication. I also implement data lineage tracking to identify and fix issues quickly.
Can you work with our existing data warehouse?
Yes, I have experience with Snowflake, BigQuery, Redshift, and traditional data warehouses. I can build pipelines that integrate with your existing data warehouse infrastructure, optimize performance, and recommend improvements where appropriate. I can also help migrate between data warehouses if needed.
What monitoring do you set up for pipelines?
I implement comprehensive monitoring including pipeline execution status, data quality metrics, processing times, error rates, and data freshness. This includes dashboards for visibility, alerts for failures and data quality issues, and automated notifications. Monitoring is set up using tools like Airflow's built-in monitoring, CloudWatch, or custom dashboards depending on your infrastructure.
How do you handle schema changes in source data?
I design pipelines with schema evolution in mind, using flexible mapping engines that can handle schema changes gracefully. This includes schema versioning, backward compatibility checks, and automated schema detection. For critical changes, I implement validation and alerting to ensure schema changes don't break downstream processes.
Ready to Build Reliable Data Pipelines?
Let's discuss how I can help you build data pipelines that ensure data accuracy and reliability at scale.