Mark – DBT, Python, SQL

A financial intelligence data platform that supplies market and asset data to internal analytics teams and external client-facing services. As data volumes and the number of consumers grew, architectural bottlenecks in the pipelines caused latency and stability issues, slowing downstream products. The platform ingests, models, and serves financial data via production-grade pipelines and API endpoints, providing analysts and applications with fast, reliable access to up-to-date intelligence.

• Leading a team of data engineers delivering production-grade Python/Airflow pipelines end-to-end; identified and removed systemic architectural bottlenecks, cutting key pipeline latency from seconds to milliseconds.
• Own the architecture roadmap, translating stakeholder and product requirements into technical plans and sprint-level delivery in an Agile setup.
• Designed API-based data consumption patterns that let internal services and external clients access data directly, keeping the platform scalable as the company grows.
• Own performance, reliability, and cost optimization of critical data workflows, improving execution stability through proactive tuning and operational best practices.
• Mentor engineers and review designs across the full pipeline lifecycle, working closely with product and analytics teams.

A data and analytics consultancy delivering cloud data platforms for S&P 500 enterprises. Client engagements center on data integration platforms built on Databricks and Google Cloud that automate the ingestion of multi-source datasets into warehouses tailored to each client's cloud strategy, compliance standards, and reporting needs. Alongside client work, the consultancy runs an internal AI assistant that lets employees query internal documentation in natural language.

• Designed and delivered Databricks and Google Cloud data integration platforms for S&P 500 clients end-to-end, automating multi-source ingestion with Apache Airflow pipelines and monitoring that kept delivery within strict SLAs.
• Advised client stakeholders on technology options and architecture patterns, translating functional and non-functional requirements into source-to-target DWH designs.
• Built transformation layers and data models with dbt, BigQuery, and PySpark for ETL, integration, and ongoing support.
• Partnered with the AI team to build an internal AI assistant in Go on the OpenAI API, enabling employees to query internal documentation in natural language.
• Replaced manual operational workflows with Python and Bash automation, improving system reliability and reducing operational effort.
• Led root-cause analysis and troubleshooting of production pipeline issues across client cloud environments.

A customer-interaction analytics platform for executive and finance stakeholders who need timely insight into customer behavior to make business and financial decisions. It streams customer interaction events in near real time into a cloud data lake and data warehouse, then exposes them through an OLAP tabular model. The model powers executive dashboards and feeds machine learning use cases.

• Designed and deployed an event-driven, near-real-time data architecture on Apache NiFi and Google BigQuery end-to-end, enabling near-real-time analysis of customer interactions.
• Cut executive dashboard load times by redesigning SSAS model partitions and optimizing DAX expressions.
• Built and optimized an SSAS tabular OLAP model that supported executive decision-making and machine learning use cases.
• Worked directly with C-level stakeholders to shape the data models and reporting behind key business and financial decisions.
• Implemented ETL flows in Apache NiFi, using BigQuery as both the data lake and the data warehouse.

A marketing data warehouse that unifies CRM, advertising platform, and web analytics data for marketing teams. With campaign and customer data spread across separate tools, reporting lagged, and audience targeting couldn't rely on a single source of truth. The warehouse runs on AWS S3 and Amazon Redshift, powering real-time KPI reporting and data-driven audience targeting for marketing leadership.

• Led a team of 3 developers building and scaling a modern data warehouse on AWS S3 and Amazon Redshift end-to-end, unifying CRM, ad-platform, and web analytics data into a single source for marketing.
• Partnered with marketing leadership to define and track real-time KPIs, reducing reporting lag and enabling data-driven audience targeting.
• Designed the ETL pipelines and data models that consolidated CRM, advertising, and web analytics sources in Redshift.

Data warehousing and reporting solutions for healthcare and government organizations that needed consolidated, reliable views of their operational data. The solutions load data from operational systems into a SQL Server warehouse and expose it through multidimensional OLAP cubes for analysis and reporting.

• Built SSIS ETL pipelines that consolidated operational data for healthcare and government clients into SQL Server data warehouses.
• Designed and delivered multidimensional SSAS models (OLAP cubes) that gave client analysts self-service reporting on that data.