126h → <30m
Critical Databricks reporting pipeline
Reduced a critical production reporting pipeline from approximately 126 hours to under 30 minutes while maintaining data accuracy.
Production Data Platform Specialist
I help data teams fix slow and expensive Databricks workloads.
I diagnose Spark and PySpark bottlenecks, optimize jobs and pipelines, improve reliability, and help US data teams use Databricks infrastructure more efficiently.
Platforms and technologies I optimize
Databricks
Microsoft Azure
Delta Lake
Snowflake
Whether your team is building a new data platform or improving an existing one, I help design, scale, and optimize Databricks environments for reliable production use.
Trusted Experience
- Enterprise Data Platforms
- Azure Databricks
- Apache Spark
- Snowflake
- US Engineering Teams
// Verified Production Results
Performance improvements measured on production data workloads.
8h → <60m
Delta ingestion/update workflow
Reduced a Delta Lake ingestion and update process from approximately 8 hours to under 60 minutes.
// Featured Case Study
Redesigning a Spark Execution Model: From 126 Hours to Under 30 Minutes
How a driver-managed, month-by-month process was redesigned as Spark-native distributed execution—restoring a critical analytics workload while preserving business correctness.
126h → <30m
Read Case Study
// Consulting Offers
Choose the level of help that matches the Databricks problem in front of your team.
Recommended Starting Point
2-3 Business Days
Databricks Performance Audit
For teams with slow Spark jobs, long pipeline runtimes, high Databricks costs, reliability concerns, or unclear bottlenecks.
Review Includes
- Spark execution and job configuration
- Cluster configuration and compute efficiency
- Delta Lake layout and partitioning strategy
- Shuffle, join, and pipeline architecture review
Outcome
A prioritized technical assessment with practical recommendations your team can act on.
New Implementations
Platform Design & Modernization
Databricks Platform Architecture
For companies designing a new Databricks platform or modernizing an existing environment across architecture, delivery, governance, and operations.
Typical Scenarios
- New implementations and platform modernization
- Azure Databricks and medallion architecture
- Data platform and scalable ingestion design
- CI/CD strategy and production best practices
- Unity Catalog and data governance
Outcome
A production-ready architecture designed for scalability, maintainability, and future performance optimization.
Recommended Engagement
1-2 Weeks
Databricks Optimization Sprint
For teams that already know a workload matters and want hands-on implementation of the highest-impact improvements.
Typical Work
- Spark, PySpark, and Delta optimization
- Cluster tuning and job optimization
- Partition improvements and reliability fixes
- Runtime and cost optimization
Outcome
Targeted engineering changes applied to the workload, with results measured against the original baseline.
Long-Term Partnership
Ongoing Support
Fractional Senior Data Engineer
For companies that need senior Databricks judgment without adding another full-time engineer right away.
Possible Responsibilities
- Technical leadership and platform architecture
- Code reviews, CI/CD, and team mentoring
- Databricks, PySpark, Azure, and Snowflake support
- Ongoing performance optimization
Outcome
Experienced engineering support for roadmap decisions, delivery quality, and production workload health.
// How We Work
A structured path from symptoms to measured improvements.
01 · Performance Review
Understand the workload and identify bottlenecks.
02 · Optimization Plan
Prioritize the highest-impact improvements.
03 · Implementation
Implement improvements together or support your engineering team.
04 · Measure Results
Validate runtime, reliability, and infrastructure improvements.
// FAQ
Common questions before scheduling a review.
Do you work with existing Databricks environments?
Yes. Most work starts inside an existing environment, with the current constraints and production realities treated as part of the problem.
Can you work with Azure Databricks?
Yes. I have strong experience with Azure data platforms, Azure Databricks, PySpark, and production pipeline operations.
Do you support Snowflake?
Yes. Snowflake support is available where it connects to the broader data platform, pipeline architecture, or Databricks workload.
Can you work with internal engineering teams?
Yes. Engagements can be hands-on implementation, advisory support, code review, or technical mentoring alongside your team.
Can you review only one pipeline?
Yes. A single important pipeline is often the right place to start, especially when it is slow, expensive, or operationally fragile.
Can you help before we hire another engineer?
Yes. Fractional support can help stabilize key workloads, improve technical direction, and reduce hiring pressure while you build the team.
// Engineering Consultant
Clear technical decisions for production data platforms.
I work with engineering leaders to turn complex platform problems into clear, prioritized decisions. My approach starts with evidence: how the system behaves, where constraints appear, and which changes will create meaningful improvement.
In production environments, architecture, reliability, and performance cannot be treated independently. I evaluate how design choices affect operations, delivery speed, data quality, and compute efficiency before recommending changes.
The goal is measurable progress: faster workloads, more predictable operations, less avoidable cost, or an architecture the team can maintain with confidence. Recommendations are validated against both technical behavior and business expectations.
// Engineering Resources
Technical articles, engineering notes, and production case studies.
Spark Performance Diaries
Practical notes from diagnosing execution plans, skew, shuffles, and production bottlenecks.
Coming Soon
Databricks Architecture Notes
Design decisions for maintainable platform foundations, governance, and reliable delivery.
Coming Soon
Production Case Studies
Technical breakdowns of platform and workload improvements, with measurable results.
Coming Soon
Performance Engineering Articles
Focused guidance on runtime, reliability, and efficient use of distributed compute.
Coming Soon
Let's identify the highest-impact next step for your data platform.
Whether you're planning a new platform or improving an existing Databricks environment, we can focus first on the architecture, reliability, or performance decision with the greatest operational impact.