Updated on 14/08/2026 Most dashboard problems are not dashboard problems. A number that does not match what someone counted by hand usually broke three steps earlier, in ingestion or a transformation nobody tested. That is where I actually spend my time as a Data and AI Engineer, and the chart at the end is the easy part. My pipelines typically run on Airflow or Mage AI, with Kafka and PyFlink handling anything that needs to move in real time. On AWS I work with Lambda, S3, EventBridge, and SNS, and bad records get pulled into a quarantine bucket instead of quietly sitting in a table someone trusts. For transformation, I build dbt models on Snowflake and PostgreSQL, structured bronze through gold, with schema tests and business rule checks written into the models themselves, so a broken assumption gets caught in the pipeline instead of by whoever opens the report next. Reporting comes after the data is solid. I build in Power BI or Tableau around the one question the business actually needs answered, not a stack of generic rollups nobody reads. When the need is document search or research rather than dashboards, I build RAG systems that score their own retrieval accuracy, so a weak answer gets flagged instead of handed over as confident nonsense. I am currently applying this same thinking at Genix Pharma, building AI-assisted workflows on local LLMs through Ollama for model experimentation, evaluation, and automated reporting. A few things I have shipped recently: a district-level KPI dashboard on Snowflake and Power BI built from layered dbt models, incremental dbt pipelines feeding logistics and lending risk reporting, a real-time Kafka and PyFlink pipeline with event-time processing sinking to PostgreSQL, and a serverless AWS pipeline where a quarantine bucket keeps bad files from ever reaching the tables people query. If a tool is not something I have genuinely used, I will say so instead of guessing my way through your job. Tell me what the reporting needs to answer and where your data lives right now, whether that is Excel, PDFs, or a handful of systems that do not talk to each other, and I will give you a straight read on whether it is a small fix or a bigger rebuild. Machine Learning, Database Design, Delta Lake Expert, Databricks Engineer, Big Data Consultant, AWS Data Specialist, Database Architecture, Amazon Web Services, Artificial Intelligence, Deep Learning Modeling, Machine Learning Engineer, Data Analytics & Visualization Software, Data Warehousing & ETL Software Data Processing, Cloud Engineering, GCP Analytics, Data Analytics, Data Visualization, Spark Developer, ETL, SQL, Python, DBT, Snowflake, Apache Airflow, Apache Kafka, AWS, Data Pipeline, Power BI, Python, Snowflake, ETL, Big Data, ETL Pipeline, Data Engineer, ETL Developer, Data Science, Data Analysis, Deep Learning, Data Engineering, Azure Databricks, MLOps Engineer

I’m an Analytics Engineer and Cloud Data professional with 17+ years of experience across healthcare analytics, data engineering, cloud integration, BI, governance, and secure data platforms. My work spans Azure, Microsoft Fabric, ADF, Synapse, Databricks, Snowflake, SQL, Python, HL7, FHIR, IoMT pipelines, HIPAA-aware data systems, and AI-ready analytics workflows. I specialize in building the analytics layer between raw data and business decisions: clean data models, trusted KPIs, governed datasets, secure pipelines, and dashboards that leadership teams can actually rely on. 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐰𝐢𝐭𝐡 ✅ 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐈 Clinical, operational, IoMT, device telemetry, EHR, HL7, FHIR, HIPAA-aware dashboards, executive reporting, and healthcare data quality. ✅ 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 SaaS metrics, eCommerce analytics, customer analytics, marketing analytics, revenue reporting, KPI frameworks, semantic models, and BI-ready datasets. ✅ 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Azure Data Factory, Microsoft Fabric, Synapse, Databricks, Snowflake, SQL, Python, API integrations, data ingestion, ETL/ELT pipelines, and cloud data modernization. ✅ 𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐭𝐚 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 AI-ready datasets, anomaly detection support, ML-ready pipelines, automated reporting workflows, intelligent insights, and structured data foundations for AI use cases. ✅ 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 HIPAA, HITRUST, NIST, RBAC, data lineage, metadata, auditability, data validation, access controls, and secure-by-design analytics architecture. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬 👉 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Designed secure Azure-based pipelines for clinical, operational, IoMT, and medical device data using ADF, Synapse, ADLS, Databricks, API integrations, HL7, and FHIR standards. 👉 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐁𝐈 𝐚𝐧𝐝 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 Delivered dashboards combining operational, clinical, device, and risk indicators to help leadership teams monitor performance, reliability, and compliance. 👉 𝐂𝐥𝐨𝐮𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Built governed data layers, SQL models, data mappings, validation workflows, and reporting-ready datasets across Azure, Fabric, Synapse, Databricks, Snowflake, and SQL-based environments. 👉 𝐀𝐈-𝐫𝐞𝐚𝐝𝐲 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 Prepared structured and reliable datasets for anomaly detection, predictive analytics, device monitoring, and advanced healthcare insights. 👉 𝐒𝐞𝐜𝐮𝐫𝐞 𝐝𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 Implemented data quality, lineage, metadata, retention, identity controls, and compliance-aligned processes for sensitive healthcare and enterprise datasets. 𝐌𝐲 𝐠𝐨 𝐭𝐨 𝐓𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 Azure Data Factory, Microsoft Fabric, Azure Synapse, Azure SQL, ADLS Gen2, Databricks, Snowflake, SQL, Python, PySpark, dbt, Power BI, Tableau, HL7, FHIR, EPIC, IoMT, APIs, Event Hub, Azure IoT Hub, Delta Lake, RBAC, IAM, HIPAA, HITRUST, NIST, and data governance frameworks. If your team needs cleaner healthcare analytics, better BI models, secure cloud data pipelines, or AI-ready datasets, send me a message and I’ll help map the right analytics architecture for your business.

Enterprises hire me when their Looker instance is broken, their LookML is unmaintainable, or their team can't deliver dashboards fast enough. I build and manage Looker platforms on BigQuery and Snowflake — 15 years in BI, the last 5 focused entirely on Looker. I managed Upwork’s own Looker platform for nearly 3 years — data modeling, dashboarding, administration, embedding, automations, and governance for their Finance Systems org on Snowflake. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐋𝐨𝐨𝐤𝐞𝐫 𝐛𝐮𝐢𝐥𝐝𝐬 (what enterprises actually use daily) → Finance & revenue reporting (P&L, billing, ARR/MRR, cash flow) on Snowflake or BigQuery → Operational KPI dashboards with drill-down: executive → department → team → individual → Marketing attribution & funnel analytics (GA4, CRM, ad platforms into Looker via Fivetran) → SaaS metrics (cohort retention, churn, LTV, CAC) with LookML semantic layer → Embedded Looker analytics (SSO embed, Looker API) for customer-facing products → Tableau → Looker, Power BI → Looker full migrations with dashboard recreation 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐦𝐲 𝐋𝐨𝐨𝐤𝐌𝐋 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 → DRY LookML: extends, refinements, object inheritance — no copy-paste models → Explore design: access filters, always_filter, sql_always_where for row-level security → PDTs and incremental PDTs tuned for performance → Liquid parameters and templated filters for dynamic dashboards → Data tests and LookML validation in CI/CD → Git workflow: feature branches, PR reviews, dev → staging → prod → Looker admin: user provisioning, content management, performance monitoring → Embedding: SSO embed, private embed, API-driven customer-facing analytics → Automations: scheduled deliveries, alerts, System Activity monitoring → Conversational analytics: Looker + LLM for natural language exploration 𝐏𝐫𝐨𝐨𝐟 → Upwork’s Looker platform ~3 years: modeling, admin, embedding, automations, governance on Snowflake. 1,072 hrs, 5.0 ⭐ → 1,323 hrs for enterprise client: rebuilt Looker instance, optimized dashboards, migrated Redshift → Snowflake, trained team on LookML → Migrated GumGum’s Looker from Redshift to Snowflake → 20+ enterprise Looker implementations across fintech, insurance, healthcare, ecommerce, SaaS, travel, media → ~100 co-dev sessions with client Looker teams → $400K+ earned, 7,000+ hrs, 100% JSS, Expert-Vetted (top 1%) 🔹 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 → 𝐂𝐨𝐫𝐞 𝐋𝐨𝐨𝐤𝐞𝐫 𝐃𝐞𝐯 — LookML modeling, explores, PDTs, caching, Looker API, embedded analytics, dashboards → 𝐋𝐨𝐨𝐤𝐞𝐫 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐌𝐠𝐦𝐭 — Admin, governance, user provisioning, content lifecycle, performance monitoring. Monthly retainer → 𝐂𝐨-𝐃𝐞𝐯 𝐒𝐞𝐬𝐬𝐢𝐨𝐧𝐬 — ~100 delivered. 60-min live pairing on YOUR LookML codebase. We ship code every session → 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐔𝐬𝐞𝐫 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 — Self-service explores, dashboards, scheduling, conversational analytics. 20+ enterprise teams trained → 𝐋𝐨𝐨𝐤𝐞𝐫 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 — Tableau/Power BI/MicroStrategy → Looker. Source audit, schema mapping, LookML generation, validation 🔹 𝐀𝐈 𝐓𝐨𝐨𝐥𝐛𝐨𝐱 Custom MCP servers that let AI agents interact with Looker, BigQuery, Snowflake: → AI-automated LookML code reviews → Tableau → Looker migrations 10x faster via MCP + AI → AI-generated Looker documentation → Natural language → LookML/SQL → Conversational analytics: Looker + Claude AI 🔹 𝐃𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 → BigQuery and Snowflake (primary), Redshift, PostgreSQL → dbt / Dataform for transformations → Fivetran / Stitch / Airbyte for ingestion → Looker Studio for GA4, Google Ads, Google Sheets reporting 🔹 𝐁𝐞𝐬𝐭 𝐟𝐢𝐭 → Enterprises needing LookML development, Looker platform management, or team upskilling → Companies migrating from Tableau/Power BI/MicroStrategy to Looker → Teams wanting a Looker expert who can also serve as Fractional CDO 🔹 𝐍𝐨𝐭 𝐚 𝐟𝐢𝐭 → One-off builds with unclear requirements or no warehouse → Looker Studio-only projects under $1K → Full-time roles — I work fractional or project-based 𝐍𝐞𝐱𝐭 𝐬𝐭𝐞𝐩 Message me: your Looker challenge, warehouse (BigQuery/Snowflake), and team size. I’ll reply with a 1-page action plan + estimate.

If you’re looking for a Data Engineer who can build scalable data pipelines and deliver actionable analytics, dashboards, and business insights on GCP/AWS, you’re in the right place. I’m a Google Cloud Certified Data Engineer with 6+ years of experience designing and deploying end-to-end ETL/ELT pipelines, modern data warehouses, and business intelligence solutions that turn raw data into meaningful insights. I help businesses centralize, transform, and activate their data - enabling better reporting, faster decision-making, and scalable analytics systems. 🚀 What I Bring • Design and build scalable batch & real-time data pipelines (BigQuery, Dataflow, Pub/Sub) • Architect modern data warehouses with optimized data models (star schema, medallion architecture) • Develop robust ETL/ELT workflows using Python, SQL, dbt, and Apache Airflow • Build interactive dashboards and reports using Looker, Power BI, and Tableau • Deliver KPI tracking, business intelligence, and data-driven insights • Transform raw data into analytics-ready datasets for reporting and decision-making • Implement data orchestration & deployment pipelines (Airflow, Kubernetes, CI/CD) • Optimize BigQuery performance & cost efficiency • Integrate marketing & SEO data sources (Google Analytics, DataforSEO, Screaming Frog, AWR) • Migrate data from on-premise systems to cloud platforms (GCP/AWS/Azure) 🧠 Specialized Expertise • Modern Data Stack: dbt, BigQuery, Airflow • Data Modeling: Logical & Physical modeling, DWH lifecycle • Data Integration: Batch & streaming pipelines • Analytics Engineering: Clean, testable, and scalable transformations • Business Intelligence & Analytics: Dashboarding, KPI reporting, data visualization • SEO & Marketing Data Pipelines (high-demand niche) 📊 Tools & Technologies • Cloud: GCP (BigQuery, Dataflow, Composer), AWS, Azure • Languages: Python, SQL • Orchestration: Apache Airflow, Cloud Composer, Kubernetes • Warehousing: BigQuery, Snowflake, Redshift, PostgreSQL • BI & Visualization: Looker (Looker Studio), Power BI, Tableau (dashboards, KPI reporting) • Data Tools: dbt, Dataform, Cloud Data Fusion 🎯 What You Can Expect • Clean, scalable, and well-documented data solutions • Strong focus on performance, cost optimization, and reliability • Clear communication and a long-term collaboration mindset 💬 Let’s Work Together If you need help building data pipelines, optimizing your data warehouse, or creating dashboards and analytics solutions, feel free to send a message - I’d be happy to discuss your project. Keywords Data Engineer, ETL, ELT, BigQuery, Apache Airflow, dbt, Data Warehousing, Modern Data Stack, GCP, Google Cloud Platform, Data Modeling, Analytics Engineering, Dataflow, Pub/Sub, Cloud Composer, Snowflake, Amazon Redshift, PostgreSQL, Python, SQL, Batch Processing, Real-time Data Processing, Data Architecture, Data Lake, Data Lakehouse, Medallion Architecture, Data Orchestration, Workflow Orchestration, Cloud Data Fusion, Dataproc, dbt Cloud, Incremental Models, SQL Optimization, BigQuery Optimization, Cost Optimization, Performance Tuning, Data Transformation, Business Intelligence, Data Analytics, Dashboarding, Data Visualization, KPI Reporting, Looker, Looker Studio, Power BI, Tableau

I build end-to-end data platforms — from raw source ingestion through dbt transformation to production dashboards — on Snowflake, BigQuery, Databricks, Sigma Computing, and Looker. One consultant who owns the full stack: ETL pipelines, cloud data warehouse, data modeling, and the BI reporting layer your team actually trusts. Founder of Warehows Analytics. Official partner with Snowflake, dbt, Databricks, and Sigma Computing. 32+ projects delivered across SaaS, e-commerce, fintech, healthtech, cybersecurity, and private equity. Top Rated Plus with 100% client satisfaction. $2.3M+ saved in client infrastructure costs. Stack: Snowflake, BigQuery, Databricks, dbt, Airflow, Fivetran, Airbyte, Hevo, Sigma Computing, Looker, Power BI, Superset, Streamlit, Python, SQL, FastAPI What I have delivered: - Fortune 500 — Oracle to Snowflake migration. 10M+ records/day CDC pipelines. Query times down 85%, costs down 60%. - $50M e-commerce brand — unified Google Ads, Facebook, email, and organic data. Single attribution model. ROAS from 3:1 to 7:1. - Finance SaaS (finsightsai.tech) — multi-tenant embedded analytics on Snowflake + Sigma serving 80+ customers. Sub-second queries. Built end to end: ingestion, dbt models, UI, LLM-powered insights. - Multi-brand e-commerce (4 brands) — Shopify, WooCommerce, QuickBooks, and ad platforms consolidated into Databricks + Looker. +65% marketing ROI. 80% less reporting time. - Cybersecurity startup — BigQuery warehouse consolidating Salesforce, RB2B, and product data. dbt-modeled attribution resolved three conflicting definitions of "converted lead." - Creative & PR agency — 50K+ customer reviews processed. AI sentiment analysis. Sigma dashboards. Client onboarding from 6 weeks to 1 week. - Veterinary clinic group — manual Excel to live cross-clinic dashboards. Airbyte + Airflow + dbt + Snowflake + Sigma. 10x faster onboarding. - Energy PE firm — Snowflake Cortex Analyst. Natural-language queries over sensitive portfolio data. - eMarketer — Snowflake reporting replacing 3-week manual Excel process. Runs daily, untouched. Services: - Cloud data warehouse design (Snowflake, BigQuery, Databricks) - ETL/ELT pipelines (Fivetran, Airbyte, Hevo, custom Python) - dbt transformation with testing, documentation, and semantic layer - BI dashboard development (Sigma, Looker, Power BI, Superset, Streamlit) - Embedded analytics for SaaS products (multi-tenant, row-level security) - Marketing attribution and revenue reporting - Data migration from legacy systems (Oracle, Talend, on-prem) - AI/LLM integration (RAG, conversational analytics, Cortex Analyst) Why this matters: data projects fail when nobody owns both ends. The pipeline engineer builds what was specified. The analyst reports what was delivered. Nobody checks whether either matches what the business needed. I hold the full picture — from raw source to executive dashboard — so the numbers your team sees are the numbers your finance team agreed to. Every model has tests. Every pipeline has monitoring. Every dashboard traces to a definition settled before the first chart was built. Send me a message. I respond the same day. Top Rated Plus · 32+ Projects · Snowflake Partner · dbt Partner · Databricks Partner · Sigma Computing Partner

If you’re struggling with broken ETL pipelines, unreliable reports, or data scattered across tools. I build production-grade data engineering solutions that your business can trust. 🚀 What I Specialize In End-to-End Data Engineering & Warehousing I design and implement modern data warehousing and ETL/ELT architectures using best practices in data modeling, performance optimization, and automation. Core Expertise: -Data Engineering & Data Warehousing -ETL / ELT Pipeline Design & Optimization -Data Modeling (Analytics & BI-ready schemas) -Cloud Databases & Scalable Architectures Automation & Orchestration I build fully automated pipelines that are reliable, observable, and easy to maintain. Tools & Technologies: -Apache Airflow (including AWS MWAA) for orchestration -dbt for transformation, testing, and documentation -AWS Glue for serverless ETL -SQL-based transformations for performance and clarity AWS & Cloud Data Platforms I architect cloud-native solutions on AWS that scale with your business. AWS Stack: -Amazon S3 for data lakes -Amazon Redshift for analytics & BI -Cloud Databases optimized for reporting workloads -Secure, cost-efficient, and scalable deployments -Business Intelligence & Analytics I don’t just move data — I make it useful. -Business Intelligence pipelines optimized for reporting -Power BI-ready data models -Analytics tables designed for fast, reliable querying -Clean, consistent metrics across teams 💡 Case Study: Large-Scale Data Pipeline Automation Challenge: Manual reporting, inconsistent data, and multiple disconnected systems causing delays and errors. Solution: Designed a centralized data warehouse on Amazon Redshift Built automated ETL pipelines using Airflow, AWS Glue, and SQL Implemented dbt for standardized transformations and testing Delivered analytics-ready datasets for Business Intelligence teams Impact: ✅ Reliable, automated data pipelines ✅ Faster reporting cycles ✅ Clean, trusted metrics for decision-making ✅ Reduced manual effort and operational overhead ✅ Why Clients Work With Me Strong focus on data quality & reliability Clean, maintainable, production-ready code Clear communication & structured delivery Experience working with growing and data-driven teams 📩 Let’s Talk If you need help with ETL pipelines, Airflow automation, dbt transformations, AWS data warehousing, or BI-ready data models, let’s discuss your use case. I’m happy to review your current setup and suggest the best path forward.

I’m Adeola — a marketing data engineer focused on one thing: fixing ad tracking and attribution. I help 8-figure eCommerce brands and venture-backed startups rebuild their marketing data from the ground up — centralizing ad, CRM, and analytics data into one reliable, connected source of truth — as well as automating offline and server-side tracking based on first-party data to optimize every dollar you spend. 🚀 Why Clients Hire Me Most engineers don’t understand business and marketing. Most marketers don’t have the technical depth. I understand both. I started as a software engineer (Computer Science) before transitioning into marketing technology, where I’ve spent years building end-to-end tracking and data systems. That combination allows me to think creatively beyond platform limitations, solve complex problems others avoid. I’m also uniquely positioned because I’ve worked across a vast stack of tools — see the full list below. 💡 What I Do I’ve implemented the following systems across a wide range of industries and niches: • Ad Tracking & Attribution – implementing advanced first-party tracking systems that ensure accurate, deduplicated, and high event-match-quality conversions across every ad platform. From GTM (Web & Server via Stape) to fully custom server-side integrations that send events directly to the Meta Conversions API, Google Ads API, and LinkedIn API. • Customer Data Infrastructure – implementing CDPs like Segment, Rudderstack, and Customer.io to unify clickstream tracking and create a single source of truth — enabling you to activate customers based on the actions they take. • Data Warehousing & Pipelines – building automated data flows with Fivetran and Airbyte, syncing platforms like Stripe, Shopify, HubSpot, and Calendly into BigQuery or Snowflake for centralized analytics. • Data Modeling – using SQL, DBT, and Dataform to standardize and transform raw data from multiple marketing and sales platforms inside the data warehouse. I design modular models that answer complex business questions around ROAS, CAC, retention, churn, attribution, and customer behavior. • Reverse ETL & Automation – syncing first-party, highly targeted data from your warehouse back into Google Ads, Meta Ads, and other platforms for advanced audience targeting and optimization — as well as pushing enriched data into HubSpot, Customer.io, and similar tools to activate customers with personalized messaging. I leverage Hightouch, Airbyte, and Fivetran to do so. • Serverless Data Architecture – extending and automating systems using Cloudflare Workers, Queues, Google Cloud Run, and event-driven infrastructure for high scalability and reliability. 🧰 Full List of Tools & Tech I’ve Worked With 🧠 CDPs & Tracking Segment (Certified Solutions Architect) · Rudderstack · Customer.io · Google Tag Manager (Web & Server via Stape) · Meta Conversions API · Google Ads API · LinkedIn API · Hyros · Voluum · Mixpanel · Amplitude 💼 CRM, Marketing & Sales Platforms HubSpot · Salesforce · Keap · Pipedrive · Klaviyo · ActiveCampaign · Stripe · Shopify · Typeform · BigCommerce · GoHighLevel (I’ve also worked directly with the underlying APIs for each.) ⚙️ Automation & Workflow Orchestration Make.com · Zapier · n8n 🏗️ Data Warehousing BigQuery · Snowflake 🔄 ETL, Reverse ETL & Data Modeling Fivetran · Airbyte · Hightouch · HevoData · DBT · Dataform 📊 Analytics & Visualization Metabase · Looker Studio · Tableau · Superset · GA4 🧩 Serverless Architecture & Engineering Infrastructure Cloudflare Workers · Cloudflare Queues · Cloudflare Workflows · Cloudflare R2 · Durable Objects · Google Cloud Run · Cloud Functions · Google Cloud Pub/Sub · Eventarc · Event-Driven Infrastructure · Node.js Servers · Custom API Integrations · Webhooks 💻 Software Engineering Stack JavaScript (React, Next.js, Node.js) · Python (Django, Pandas, Streamlit) · Golang · SQL

I learn how your business works and build the analytics your team needs to run it. I've done this for 50+ clients across financial services, healthcare, staffing, manufacturing, and e-commerce. Every engagement follows a proven methodology designed to get you from first conversation to trusted reporting as fast as possible. This is what you get when working with me: 1. A clear starting point. We have a focused conversation where, together, we figure out what decisions your team needs to make and what information is missing. You walk away with a requirements document you can sign off on or provide feedback on. No ambiguity about what's being built or why. 2. A prioritized roadmap. I audit your systems, your data, and your current reporting. You get a clear picture of what drives real value, what can wait, and what questions your team should be asking. The noise gets separated from what matters. 3. A working product with your real data, fast. Your systems get connected, your data gets structured, and within days you're looking at your own numbers in something real. Your dashboards tell a story: what's happening, why it's moving, where to focus. When someone wants a different cut or a new question answered, the system handles it without a rebuild. 4. Answers you trust. Data quality is built into the system from day one. When a number looks off, your team can trace it to the source. When a metric moves, you'll know whether it's a real change in behavior or just the mix shifting underneath. Findings are clear. Next steps are obvious. 5. Something that lasts. Full documentation, training, and structured handoffs. If you want ongoing support, I'm here. If you don't, everything is built so your team can maintain and extend it independently. My background is in economics (UCSD) and analytics (Georgia Tech MS), with certifications across the major cloud and BI platforms. 50+ engagements. Proven methodology at every phase. Let's talk about what you're trying to accomplish.

Power BI developer + Data Engineering — I build the automated pipelines (Databricks, ADF, SQL, Fivetran) and the dashboards leadership trusts, end to end. If your team is still pulling numbers by hand, stitching exports together, and quietly not trusting what's in the report — your data isn't slow, it's unbuilt. And every decision made on top of it is shakier than it should be. I'm a data engineer and BI specialist with 13+ years building the full path from raw, messy source data to the dashboards leadership actually trusts. Co-founder of GrowthBI, an analytics consultancy serving scaling companies across Australia, Europe, and the US. Here's what makes me different: most freelancers do one half. Pure data engineers hand you clean tables and no story. Pure dashboard builders make polished reports on top of data that breaks every week. I build both halves — the pipeline and the reporting — so you hire one person instead of managing two. 80%+ of my clients come back for follow-on work. Where I deliver the most impact: → Data pipelines & automation — Fivetran, DBT, SQL pipelines that ingest from SAP, CRMs, ad platforms, and spreadsheets, model the data in Databricks/BigQuery, and refresh on a schedule so reports build themselves → Data modelling & warehousing — clean, documented, single-source-of-truth tables your whole team can rely on → Power BI & Looker Studio dashboards for marketing, product, sales, and finance leadership → Executive reporting — YTD, MTD, YOY, and scenario comparisons leadership can act on → Customer & growth analytics — RFM modelling, cohort analysis, churn prediction, CAC vs. LTV, channel attribution → Row-level security (RLS) & governance for multi-team and multi-entity reporting Results I've delivered: → Consolidated [X] disconnected sources into one automated pipeline — eliminated ~[X] hrs/week of manual reporting and gave leadership numbers they could finally trust → 10x growth in customer acquisition — surfaced channel-mix insights that reshaped how a leadership team allocated marketing spend → 10% churn reduction — identified the highest-risk customer cohort with RFM segmentation and built a live re-activation tracker for the sales team → Prevented significant revenue loss — caught a payment failure from a default product setting before it compounded, with a live monitoring dashboard run until the fix shipped My core stack: SQL · DBT · Fivetran · Databricks · BigQuery · Power BI · DAX · Power Query · Alteryx · SSIS · Excel Industries: E-commerce · SaaS · Real Estate · Financial Services · Insurance · Consumer Goods Clients I've worked with: Rate My Agent · Keep It Cleaner · Montgomery Homes · Peeplcoach · Paylater · Joolca I work directly with C-suite leaders, senior leadership teams, and cross-functional analysts. I understand what each team needs to answer — and I build the data layer and the dashboards that answer it, without a manual every time.

I am naturally curious about systems, numbers, and how data comes together. I enjoy dissecting complex processes, understanding data flows end-to-end, investigating inconsistencies, and turning ambiguous data into clear, actionable insights. I’m often the person teams turn to when the numbers don’t reconcile, the data doesn’t make sense, a pipeline breaks, or a complex workflow needs to be investigated and simplified. I enjoy going beyond the surface-level problem to understand the underlying cause and build a practical, scalable solution. What I bring ✅ Data Analytics & Business Insights – Deep-dive analysis, exploratory analysis, KPI development, forecasting, and translating complex data into actionable recommendations. ✅ Data Operations & Reliability – Production pipeline monitoring, troubleshooting, data validation, reconciliation, root-cause analysis, and operational process improvement. ✅ Data Engineering & Analytics Engineering – Data ingestion, ETL/ELT workflows, data modeling, analytics-ready datasets, and scalable data platforms. ✅ Data Quality, Governance & Observability – Data quality frameworks, validation, metadata, lineage, documentation, governance standards, monitoring, and trusted data foundations. ✅ Marketing & Media Analytics – Marketing Mix Modeling (MMM), advertising analytics, audience measurement, campaign performance, revenue analytics, media data, and KPI frameworks. ✅ AI & Automation – Applying LLMs and AI tools to automate data quality, documentation, investigation, metadata discovery, and analytical workflows. Experience Over 8+ years, I have worked across data analytics, data engineering, DataOps, marketing analytics, data governance, and enterprise data platforms, partnering with business and technical teams to solve complex data problems and deliver reliable analytical solutions. My experience includes working with large and diverse datasets, production data pipelines, marketing and media ecosystems, cloud data platforms, and multiple internal and external data sources. I particularly enjoy problems where data is messy, incomplete, inconsistent, or distributed across multiple systems—and where the solution requires a combination of investigation, analytical thinking, technical understanding, and business context. Tools SQL | Python | Databricks | Snowflake | BigQuery | Redshift | dbt | Airflow | AWS | Tableau | Looker Studio | Excel | Hex | Atlan | Monte Carlo | Claude | Cursor | GPT | Databricks Genie Domains Data & Analytics | Data Operations | Marketing & Media | Advertising & AdTech | Media & Entertainment | Data Governance | Finance | SaaS I'm particularly interested in opportunities at the intersection of Data, AI, Analytics, and Business Decision-Making, where I can use both my technical understanding and analytical curiosity to solve meaningful problems. Always open to interesting data challenges, consulting opportunities, and collaborations.