I build the data infrastructure that recognises revenue at scale.
Microsoft Fabric · Azure · Power BI · Databricks · Spark · Python · SQL
Batch and streaming pipelines, governed reporting platforms and AI-driven analytics across banking, telecoms and government.
Ten years owning delivery end to end with client discovery and scoping, architecture and build, then the team that runs it.
Data engineering should change
what the business can do.
I build data systems around real business decisions — improving revenue, understanding customers, and making operations more efficient. The engineering matters, but the outcome is what counts.
Turn data into revenue
Sales forecasting, lead generation, marketing ROI and e-commerce analytics that help teams identify where revenue is coming from, where it is leaking, and where to invest next.
Make customer behaviour useful
Segmentation, behavioural analysis, loyalty and automated customer experiences that turn fragmented customer data into better targeting, retention and engagement.
Make the business run smarter
Financial processing, inventory, operational workflows and APIs that reduce manual work, improve visibility and give teams reliable data for faster decisions.
Selected work
Client names and system names are withheld under NDA. The architecture, the trade-offs and the results are not.
Telecoms · Streaming · Observability Streaming Delivery Observability
Event-driven telemetry platform for a high-volume messaging service used by retail banks. Idempotent webhook ingestion with schema validation at the boundary, micro-batched to object storage, status-reconciled in Spark and served with sub-ten-minute freshness. Gave operations the SLA evidence trail that provider credit claims had previously been argued without.
Streaming Delivery Observability
Event-driven telemetry platform for a high-volume messaging service used by retail banks. Idempotent webhook ingestion with schema validation at the boundary, micro-batched to object storage, status-reconciled in Spark and served with sub-ten-minute freshness. Gave operations the SLA evidence trail that provider credit claims had previously been argued without.
StackDockerised Spark · Azure Data Lake · Azure Blob Storage · SQL Server · Python scheduler · Power BI · webhook ingest with schema validation and IP allowlisting
- Context
- Delivery-status webhooks arriving continuously at high volume. Operations needed per-message status visibility within ten minutes to diagnose failures against provider SLAs.
- Options
- Managed message bus · managed event streaming service · containerised Spark with batched writes to object storage.
- Decision
- Containerised Spark, batching webhook payloads to object storage before processing.
- Why
- Write-cost per event dominated at projected volume; both managed options priced per message. Batching traded roughly two minutes of latency for a large reduction in storage transaction cost, comfortably inside the ten-minute requirement.
- Consequence
- Materially cheaper and simpler to operate, but the latency floor is now batch-interval-bound. Revisit if the requirement tightens below five minutes.
Business impactConverted an unmeasured operational risk into a contractual position, and made provider performance arguable with evidence.
Reported as generic. Client, platform and provider names withheld.
Finance · Governance · Access control Governed Financial Reporting Platform
Single governed semantic model over consolidated P&L, with row-level security bound to the organisational hierarchy, provisioning executed as an auditable workflow rather than administrator memory, and lineage documented from source ledger through to executive visual. Replaced a monthly spreadsheet distribution that no auditor could trace.
Governed Financial Reporting Platform
Single governed semantic model over consolidated P&L, with row-level security bound to the organisational hierarchy, provisioning executed as an auditable workflow rather than administrator memory, and lineage documented from source ledger through to executive visual. Replaced a monthly spreadsheet distribution that no auditor could trace.
StackSQL Server · automated ETL · Power BI with row-level security · Microsoft Forms provisioning workflow · Python access-management scripts
- Context
- Directors needed self-serve access to unit-level financials. Finance needed certainty that no leader could see another unit's numbers, and auditors needed evidence of that.
- Options
- Separate report per business unit · application-layer filtering · row-level security on a single governed model.
- Decision
- Single semantic model with row-level security, provisioning routed through a tracked request workflow rather than ad-hoc administration.
- Why
- Per-unit reports would have multiplied maintenance and let definitions drift apart. A single governed model keeps one definition of every metric and makes access an auditable data point rather than an administrator's memory.
- Consequence
- One model to maintain and one definition of truth, at the cost of a stricter change process — any model change now requires re-validating security roles.
Business impactDirectors self-serve unit financials without a controls exception, and month-end reporting effort collapsed to a refresh.
Reported as generic. Client and system names withheld.
Finance · Reconciliation · Revenue assurance Revenue Leakage Detection
Entity resolution across two disconnected CRM estates with no shared key. Deterministic matching on normalised identifiers, fuzzy fallback on address and asset serial, and a human-in-the-loop explorer so analysts adjudicate the residual rather than the whole set. Surfaces delivered-but-uninvoiced work orders every close cycle.
Revenue Leakage Detection
Entity resolution across two disconnected CRM estates with no shared key. Deterministic matching on normalised identifiers, fuzzy fallback on address and asset serial, and a human-in-the-loop explorer so analysts adjudicate the residual rather than the whole set. Surfaces delivered-but-uninvoiced work orders every close cycle.
StackSQL Server · ETL pipelines · fuzzy and rule-based matching · Power BI matching explorer with analyst override
Business impactRecurring monthly recovery of revenue already earned and delivered. Figures available on request.
Reported as generic. Client and system names withheld.
Platform · Reliability · Cost Platform Reliability Monitoring
Observability layer for the reporting estate. Gateway health, dataset refresh outcomes and agent job state ingested into a modelled serving layer, with alerting bound to SLO breach rather than raw failure count. Paired with a capacity scheduler that parks idle compute, so reliability and unit cost improved on the same change.
Platform Reliability Monitoring
Observability layer for the reporting estate. Gateway health, dataset refresh outcomes and agent job state ingested into a modelled serving layer, with alerting bound to SLO breach rather than raw failure count. Paired with a capacity scheduler that parks idle compute, so reliability and unit cost improved on the same change.
StackPower BI Gateway telemetry · SQL Server Agent · Python ETL · custom capacity scheduler pausing idle compute
- Context
- Refresh failures were discovered by users, not engineers. Separately, development database spend was growing without clear attribution to teams.
- Options
- Vendor monitoring add-on · manual audit cadence · build telemetry ingestion and a capacity scheduler in-house.
- Decision
- In-house telemetry pipeline plus a scheduler that pauses capacity when idle.
- Why
- Availability ranked above cost, but the vendor option priced per-workspace and would have grown with the estate. Building it kept monitoring coverage complete while the scheduler paid for the effort within months.
- Consequence
- Failures fell sharply and spend dropped roughly a third, but the telemetry pipeline is now itself a system requiring maintenance and its own monitoring.
Business impactFailures found by engineers instead of executives, and a material reduction in platform run-rate on the same piece of work.
Reported as generic. Client and platform names withheld.
The Engineer Behind the Outcomes
The Senior Data Engineer who connects technology to the decisions and outcomes that matter. Explore the work first, then get to know the engineer, experience and capabilities behind it.
Gilroy Gordon is a Data & AI leader, analytics engineer and educator with over ten years delivering measurable impact across private sector, government and nonprofit organisations internationally. He specialises in building scalable data platforms, AI-driven solutions and intelligent automation that improve revenue performance, customer experience and operational efficiency.
A multi-certified Data & Analytics professional, including Microsoft, and a Certified Scrum Master, he combines deep technical expertise with strategic leadership to help organisations translate data into competitive advantage. Gilroy has chaired AI and Data committees, developed Data and AI academic programmes at the University of Technology, Jamaica, and presented at regional and international forums including IEEE workshops and BizTech.
He remains committed to community service, mentorship and capacity building, and has been active in service organisations supporting initiatives that strengthen digital and social impact ecosystems. He is humbled to have been recognised in the Nova Scotia Legislature for contributions to Legal Aid initiatives affecting over 400,000 Canadians, to have been named Young Entrepreneur of the Year in 2020, and to keep finding opportunities in youth development.
The full record
Career history, certifications, education, publications, awards and recommendations are maintained on LinkedIn rather than duplicated here.
Learn more on LinkedIn →Case Studies
Read about the work, the decisions behind it, and what it delivered.
View Case Studies →What I work across
Layered the way a platform actually is, rather than as an inventory.
Everything else in the toolkit — languages, servers, platforms, methods
Languages
SQL · T-SQL · HiveQL · Python · C# · ASP.NET · Java · JavaScript · TypeScript · PHP · R · C · C++ · Dart (Flutter) · Solidity · Cypher · Pig Latin · Bash · HTML5 · CSS
Data & big data
Microsoft Fabric · Azure Data Factory · Azure Databricks · Azure Data Lake · OneLake · Apache Spark · Hadoop · Hive · HBase · Sqoop · Apache Pig · Impala · Zeppelin · Ambari · Kafka · Airflow · dbt · SSIS · Snowflake · KNIME
Databases
SQL Server · MySQL · PostgreSQL · SQLite · MongoDB · Neo4J · Lucene · Solr
BI & reporting
Power BI (Embedded · RLS · Gateway administration · DAX tuning) · SSAS · SSRS · Tableau · Looker Studio · SAP Crystal Reports · Jupyter · R Studio · Excel & Power Query
AI, ML & automation
MLflow · Hugging Face · LLMOps · NLP · Generative AI · recommendation systems · predictive modelling · RPA · Power Automate · web mining
Ship & run
Azure DevOps · Git · GitHub · GitLab · Bitbucket · Team Foundation Server · Subversion · Travis · Docker · Kubernetes · Ansible · Linux · Windows · Android · ARMv6
Application & servers
Node.js · React · Angular · Laravel · Bootstrap · IIS · Apache · NGINX · Tomcat · IBM WebSphere · Glassfish · WordPress · Joomla · Magento
Delivery
Agile · Scrum (Certified ScrumMaster) · Kanban · Waterfall · RAD · Lean Six Sigma (White Belt) · Project Management Essentials
Working on something that needs to be trusted?
Two doors, depending on what you need.
Talk to me directly
Full professional history, current work and experience live on LinkedIn. Best route for permanent, contract and lead engineering roles.
Bring in the team
IGonics is my consultancy. Data platforms and warehousing, analytics and BI, AI and automation, systems integration and custom software — scoped, delivered and supported by a team rather than one pair of hands.