Simon Chia Data Engineer / Architect
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NOW · architecting a $2.8M SAP BW → Databricks migration

Simon Chia

Data Engineer & Architect / Databricks Lakehouse Platforms

Enterprise data platforms that Fortune 500 finance, supply chain and operations teams run on. Twenty years in enterprise data, fifteen of them in consulting. Currently designing and delivering a multi-year SAP BW to Databricks lakehouse migration, a $2.8M program funded through 2029.

Read the flagship migration Run the live lakehouse
San Diego, California contactme@simonchia.com +1 201 565 5049
Skill sets
01

SAP data platforms

Twenty years inside the SAP estate. The job is getting data out of S/4HANA and BW, then carrying across the modeled history and the business logic that exists nowhere else in writing.

S/4HANASAP BW/4HANA DatasphereCentral Finance
02

Databricks lakehouse

Medallion design on Delta Lake under Unity Catalog governance. This is the target platform on the migration now in delivery, and where most of the current technical work sits.

Delta LakeUnity Catalog MedallionDatabricks SQL
03

Data engineering

Ingestion, dimensional modeling, quality expectations and lineage. All the work that sits between a stubborn source system and a number the business can trust.

ELT & ingestionDimensional modeling Data qualityLineage
04

Running it as a program

Scope, schedule, budget and risk on multi-year platform work. Teams of up to 23 engineers, concurrent workstreams, several vendors, reporting at client VP level.

Azure DevOpsAgile / Scrum RAIDMulti-vendor
Capabilities

Lakehouse platforms, from source system to semantic layer

The foundation underneath the dashboards, rather than the dashboards themselves. Ingestion from systems nobody designed to be left. A governed model that survives an audit. Pipelines somebody else can run on Monday morning.

01

Lakehouse architecture

Medallion design on Delta Lake. Workspace and catalog topology, storage layout, table design, and the boundaries between raw, conformed and curated that stop a lake turning into a swamp.

DatabricksDelta LakeMedallionADLS Gen2
02

SAP → lakehouse ingestion

Extraction chains out of S/4HANA and SAP BW into bronze, carrying history across instead of rebuilding it. This is the hard part of every SAP migration and the part most plans underestimate.

S/4HANASAP BWAzure Data FactoryELT
03

Data modeling

Turning 250 master data tables and 25 BW models into conformed silver and dimensional gold. Business keys, slowly changing dimensions, and a grain that answers the question being asked.

Dimensional modelingStar schemaSCDMaster data
04

Governance & Unity Catalog

Catalog and schema topology, ownership, access model, lineage and PII controls, all designed in at the start. Governance works as a delivery guardrail when it is there from the beginning. It becomes a bottleneck when it arrives at the end.

Unity CatalogLineageAccess controlPII
05

Pipelines & delivery engineering

Incremental ELT, orchestration, quality expectations and recovery paths. Built on Azure DevOps with scrum cadence, user stories and an active RAID log, so risk shows up early instead of at cutover.

Azure DevOpsCI/CDData qualityOrchestration
06

Migration strategy & business case

Feasibility, target-state options, sequencing and the technical case a funding decision rests on. This is the work that turns "we should move off BW" into a scoped, costed roadmap a business can commit budget to.

Business caseRoadmapRAIDCutover
Reference architecture

The platform, layer by layer

The shape of the SAP-to-Databricks lakehouse currently in delivery. Open any layer to see what gets built there and the decisions behind it.

Read it from the bottom up. Data moves from source systems through ingestion into bronze, gets conformed in silver, modeled in gold, and only then consumed. Governance is not a layer of its own. It cuts through all of them.

Proof

One program in depth, and one platform you can run

A résumé only makes claims. These two show the work. One is the full anatomy of the migration now in delivery. The other is a working lakehouse you can run in the browser.

Flagship case study

A $2.8M SAP BW → Databricks migration

How a legacy BW estate became a funded, sequenced lakehouse program. It covers the feasibility work, what made it fundable, the target architecture, and how 200 Power BI reports move without the business noticing.

2025 → 202912 engineers
Read it
Interactive demo

A working lakehouse, in your browser

Run a medallion pipeline end to end. Watch tasks execute, a quality expectation fail and quarantine bad rows, table lineage resolve, and a gold-layer dashboard refresh from the results. Synthetic data, real mechanics.

12 tasksrunnable
Run it
Track record

Fifteen years delivering complex data programs in consulting

Fortune 500 and enterprise clients across industrial manufacturing, animal health, agriscience, oil and gas, and utilities. The last decade of that has been in Big 4 technology consulting. Clients are anonymized by sector, as on the CV.

2025–Present
Big 4 consulting · San Diego

Analytics Delivery Lead & Program Manager, S/4HANA and SAP BW to Databricks

Global industrial manufacturer

Built the analytics roadmap and the technical business case the program was funded against, then took end-to-end ownership of delivery. Architected the S/4HANA → BW → SAP Datasphere → Databricks ingestion chain on a medallion Delta Lake foundation with Unity Catalog governance. Runs two concurrent programs for the same client and reports to the client VP.

Program funding$2.8M
Scope25 models · 250 tables · 200 reports
Teamup to 12 engineers
Horizoncompletion 2029
2024–Feb 2025
Big 4 consulting · San Diego

Technical Delivery Manager, S/4HANA analytics

Animal health

Owned scope, deliverables and client management for the analytics workstream of an S/4HANA business transformation. Resolved a contested BW-versus-Datasphere platform decision by structuring a feasibility evaluation that de-risked the choice without exposing scope, timeline or budget. Then held deadline and budget against the unknown limitations of a newly positioned SAP product.

Team9
DecisionBW vs Datasphere
Outcomede-risked, on budget
2022–2024
Big 4 consulting · San Diego

SAP Enterprise Data & Analytics Manager and Delivery Lead

Agriscience

Delivered a multi-region LATAM analytics program on time, on budget and with zero escalations in a multi-vendor environment. Ran concurrent SAP BW and Databricks workstreams with S/4HANA as source. Built the S/4HANA → BW → ADLS Gen2 → Databricks ingestion chain on a medallion architecture, which delivered governed data to the data science team for their modeling use cases.

Program value$2M
Annual budget$1.5M
Teamup to 15
Escalationszero
2020–2022
Big 4 consulting · San Diego

Data & Analytics Portfolio Delivery Manager

Oil & gas

Managed a portfolio of concurrent projects, six months to multi-year, across finance, supply chain, tax, downstream exploration, warehouse and customer domains. Delivered across a SAP-to-Azure-Data-Factory-to-Databricks stack spanning data engineering, data science and proof-of-concept work. That included a fraud detection POC built to justify further funding.

Engineers23
Domains6
StackSAP → ADF → Databricks
2019–2020
Big 4 consulting · San Diego

Project & Engagement Manager, SAP Finance & Analytics

Power & utilities

Ran a SAP BW/4HANA build calculating taxable revenue from locality-based customer billing, where regulatory accuracy was the deliverable. Integrated SAP S/4HANA IS-U, Thomson Reuters ONESOURCE and SAP ECC (FI/CO) into a single compliant reporting solution for tax remittance, accrual and payment.

PlatformSAP BW/4HANA
IntegrationsIS-U · ONESOURCE · ECC
Constraintregulatory accuracy
2016–2018
Big 4 consulting · Melbourne

Analytics Delivery Lead, SAP S/4HANA Central Finance

Premium beverage

Led S/4HANA Central Finance standardization across regulated alcohol and non-alcohol divisions in a dual-regulatory environment, identifying the requirements at risk of delaying the program and driving them to resolution to protect regulated financial reporting controls.

ScopeCentral Finance
Environmentdual-regulatory
Stack

Platforms, tools and certifications

Lakehouse & cloudcore
Databricks Delta Lake Unity Catalog Medallion architecture Databricks SQL Microsoft Azure ADLS Gen2 Azure Data Factory
SAP estatesource side
SAP S/4HANA SAP BW/4HANA SAP BW 7.5 SAP Datasphere SAP HANA SAP Analytics Cloud SAP Central Finance ECC FI/CO IS-U
Modeling & governancethe discipline
Dimensional modeling Data warehousing Data integration & ingestion ETL / ELT Data governance Data quality Lineage PII & confidential data protection Master data
Deliveryhow it ships
Azure DevOps Agile / Scrum Program & portfolio delivery Business case & roadmap RAID & risk management RICEFW Proposals & SOWs Steering committee reporting Power BI
Certifications
DB Databricks Certified Data Engineer Associate● in progress
P2 PRINCE2 · Project Managementcertified
SAP SAP Business Data Cloud (BDC)2025
SAP SAP HANA · SAP BW 7.5certified
About

Twenty years of building the layer nobody sees

Simon Chia hiking in Sedona, Arizona
Simon Chia with family
San Diego, California Big 4 consulting · 2016–present

I started in enterprise data when the warehouse was a SAP BW cube and a nightly batch window. Twenty years later I am still on the same problem, in progressively harder forms. How do you get trustworthy data out of systems that were never designed to give it up, and keep it trustworthy once it is out?

Fifteen of those years have been in consulting, the last decade of it in Big 4 technology consulting. The programs are Fortune 500 ones where the data platform is load-bearing, like regulated tax reporting, multi-region supply chain and financial consolidation. The work is equal parts architecture and execution. Somebody has to design the medallion model and the ingestion chain. Somebody also has to sequence the delivery, hold a team of twenty-three to it, and explain to a CFO why the history has to come across instead of being rebuilt.

Right now I am doing both on a SAP BW to Databricks lakehouse migration. That is 25 data models, 250 master data tables and around 200 Power BI reports moving onto Delta Lake under Unity Catalog, funded through 2029. It is the most interesting problem I have had. It is also why I went deep on Databricks, because the platform finally makes that architecture buildable.

Postgraduate in Management
University of Melbourne Business School · 2015
B.Sc. (Hons), Computing
Staffordshire University
Client organizations
BPJohnson & JohnsonBritish American Tobacco Owens CorningDiageoCortevaOrigin Energy Asahi BeveragesThames WaterFlorida Power & Light Medibank Health Services
Résumé

Download the CV

Everything substantive is already on this page. The PDF is the formal version, with dates, certifications and education in one file. Leave a name, an email and a phone number and the download starts straight away.

Used to know who is asking and to follow up. Not shared with anyone else and not added to any mailing list.

Contact

Building a lakehouse,
or migrating off SAP BW?

That's the problem I'm deepest in right now. If you're hiring for a senior or lead data engineering role, or you need someone who can architect the platform and also land the program, I'd like to hear about it.

Email me Download résumé (PDF)
Emailcontactme@simonchia.com → Phone+1 201 565 5049 → RésuméRequest the PDF →
Based inSan Diego, California ·