The Impact of Great ETL &
Data Modeling
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5x faster reporting
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30% reduction in infrastructure costs
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99% pipeline success rate
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3x easier onboarding for new analysts
Data Pipelines - Extract. Transform. Load.
From Source to Solution: Building Reliable, Scalable Data Pipelines
Data pipelines are the foundation of modern analytics. I design ETL systems that reliably move and transform data from raw, disparate sources into clean, business-ready datasets — fast, accurate, and highly maintainable.
01
Source Integration
​Connect to diverse data sources — cloud platforms, databases, APIs, file systems — using optimized extraction strategies. Whether it's Salesforce, SAP, PostgreSQL, or custom web APIs, I build my pipelines to handle volume and variety.
02
Data Extraction
Implement incremental loads, CDC (Change Data Capture), and real-time ingestion where appropriate.
03
Data Transformation
Cleanse, enrich, and standardize datasets using a combination of SQL transformations, dbt modeling, or cloud-native tools. I enforce data validation rules early to catch errors before they cascade downstream (i.e. Data Type Validation, De-duplication, Formating, etc.)
04
Load & Schedule
Load curated data into high-performance data warehouses like Snowflake, Azure, or BigQuery, — ready for use by BI platforms, ML models, or operational tools.
Data Model Architecture
Clean data is only useful if it's structured for fast, flexible analysis. I build dimensional data models that deliver consistent KPIs, faster queries, and intuitive reporting experiences — no matter how complex the source systems.
01
Dimensional Modeling Techniques
I design models using best practices like Star Schema and Snowflake Schema architectures. These structures organize data around clear business processes — such as Sales, Marketing, and Operations — enabling easier self-service reporting and consistent insights.
02
Relationship Management
Carefully define relationships between entities to ensure fast joins, optimized SQL queries, and no data duplication or mismatches.
03
Handling Data Evolution
I account for real-world changes using techniques like Slowly Changing Dimensions (SCD) — so when a customer changes industry or a product price changes, history is preserved accurately for reporting.
04
Aggregations and Optimization
For large datasets, I design aggregated tables and materialized views to drastically improve query speed — enabling efficiency without sacrificing detail.
05
Semantic Layer
I often build a semantic layer on top of the physical model (especially in Power BI) when appropriate — defining consistent business metrics like "Net Revenue," "Churn Rate," "Customer Growth Rate" — ensuring all users get the same answers to the same question to drive data consistency and trust.