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Data Engineering, Analytics, and Applied AI Consulting

I help engineering-led companies design, fix, and scale data platforms that are reliable, cost-efficient, and ready for analytics and applied AI.

Principal Engineer at a leading fintech

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With over 15 years of experience in the industry, I currently work as a Principal Engineer at a leading fintech. I design and operate large-scale, production data systems under real constraints: cost, reliability, compliance, and growth.

This consulting work brings the same level of rigor, systems thinking, and hands-on execution to teams that need senior expertise without building it all internally.

Not generic consulting. Focused, outcome-driven work grounded in real production experience.

What I Work On

I help teams across the full lifecycle of modern data platforms, from first principles to large-scale systems.

Data Engineering and Streaming Systems

  • Kafka-based event pipelines and stream processing
  • Spark and Spark Streaming for batch and near-real-time workloads
  • Incremental, scalable data transformations
  • Pipeline reliability, idempotency, and replayability
  • Observability and failure detection for data systems

Analytics and Data Platforms

  • Data warehouse and lakehouse design
  • Snowflake and BigQuery optimization
  • Data modeling for trustworthy analytics
  • Analytics engineering and transformation layers
  • Data platform design for multiple teams and use cases

Data Architecture and Governance

  • End-to-end data architecture design
  • Data lake and data lakehouse architectures
  • Data contracts, schemas, and ownership models
  • Governance, lineage, and access control
  • Designing for compliance without killing velocity

Applied AI, Machine Learning, and MLOps

  • Data foundations for applied AI and ML
  • Feature pipelines and feature stores
  • Training and inference data workflows
  • Model lifecycle management and MLOps foundations
  • Identifying high-leverage AI and ML use cases grounded in business reality

Cloud Platforms and Cost Optimization

  • Building data platforms on AWS and GCP
  • Cloud-native architecture and tooling choices
  • Cost optimization across compute, storage, and data warehouses
  • Designing systems that scale predictably without runaway costs

Building Data Functions From Scratch

  • Designing the first production-ready data stack
  • Choosing tools that fit team size and maturity
  • Establishing standards, best practices, and ownership
  • Avoiding early architectural mistakes that are expensive to undo

What I can do for you

1. Data Architecture and Platform Audit

A short, high-impact engagement to understand what is really happening in your data systems.

Common triggers:

  • • Analytics are not trusted by stakeholders
  • • Kafka or Spark pipelines are fragile or opaque
  • • Snowflake or BigQuery costs are escalating
  • • AI or ML initiatives are blocked by data issues
  • • Legacy pipelines are holding the team back

What you get:

  • • End-to-end review of your data architecture and pipelines
  • • Identification of reliability, scalability, and cost bottlenecks
  • • Clear prioritization of fixes and improvements
  • • A written technical report with concrete recommendations
  • • A walkthrough call aligned to your team and constraints

This is often the fastest way to create clarity and momentum.

2. Data Platform Optimization or Modernization Sprint

Focused execution to materially improve part or all of your data platform.

Examples:

  • • Kafka and streaming pipeline hardening
  • • Spark job optimization and simplification
  • • Warehouse and query cost reduction
  • • Migration from legacy ETL to modern data stacks
  • • Data model redesign for analytics or ML workloads

Work is scoped, time-boxed, and measured by outcomes, not activity.

3. Fractional Principal Data Engineer or Architect

Ongoing senior-level support without a full-time hire.

I work closely with your team on:

  • • Architecture and design decisions
  • • High-risk or high-impact pipelines
  • • Streaming and real-time systems
  • • Analytics and AI data foundations
  • • Technical strategy as the team scales

This works especially well for teams that need experience and judgment more than raw execution capacity.

Who This Is For

This work is a strong fit if you are:

  • A seed to Series B startup or growth-stage company
  • An engineering-led organization with real production data
  • Running or moving toward Kafka, Spark, Snowflake, BigQuery, or cloud-native platforms
  • Investing in analytics or applied AI and want the data foundations done right
  • Looking for senior, hands-on expertise rather than a large consultancy

Who This Is Not For

This is likely not a good fit if you are:

  • ✕Looking for the cheapest possible implementation
  • ✕Expecting generic dashboards or no-code solutions
  • ✕Very early-stage with no production data
  • ✕Not prepared to act on technical recommendations

Being explicit here saves time on both sides.

How We Typically Work Together

  1. 1Short discovery call to understand context and goals
  2. 2Audit or scoped sprint to establish clarity and trust
  3. 3Deeper execution or ongoing advisory if there is a strong fit

Clear scope, clear timelines, and no open-ended consulting.