Skip to content
IO
Open to select advisory workEdmonton, Alberta, Canada

IkennaChuksOkoloMSc

I build the data and AI platforms that enterprises bet on.

Senior Manager for Cloud, Data & AI Engineering at PwC Canada. Formerly data engineering lead at Google. Twelve years moving mission-critical estates onto GCP, AWS and Azure — with the certifications to match.

Ask my digital twinCareer journeyGet in touch
IO
Currently

Senior ManagerCloud, Data & AI Engineering

PwC Canada

Focus
Agentic AI · Lakehouse · MLOps
Clouds
GCP · AWS · Azure · Databricks
Based
Edmonton, Canada
12+
Years in data engineering
3
Hyperscalers, certified
3+
Years building at Google
20+
Professional certifications

Built at

  • Google
  • PwC Canada
  • Deloitte
  • Life360
  • ATB Financial
  • MedWatch
  • Government of Alberta
  • TradeDepot
01About

A decade of building the layer everything else depends on.

Over a decade designing and deploying enterprise data estates across GCP, AWS and Azure. As a former Google senior engineer and now a PwC Senior Manager, I lead the migration of mission-critical workloads into the cloud with a focus on 99.99% reliability, security compliance and production-grade MLOps.

I move fluently across real-time streaming, lakehouse architecture and infrastructure-as-code — Dataflow, Pub/Sub, Kinesis, Databricks, Terraform, Kubernetes — with deep roots in Python, Java, Scala and SQL. What I care about is the part most teams skip: turning messy, contested, high-volume data into systems that stakeholders actually trust.

Today I lead cloud, data and AI engineering at PwC Canada, translating hard technical strategy into delivery that clears audit, scales, and holds up in production.

Anyone can move data. The work is making an organisation agree on what it means — and then keeping that true at scale.

Operating principles

  • 01

    Architecture before code

    The expensive mistakes are made in the first week, not the last sprint.

  • 02

    Trust is the deliverable

    A pipeline nobody believes is a pipeline nobody uses. Quality and lineage are features.

  • 03

    Simple scales

    Fewer moving parts, sharper contracts, and boring infrastructure that survives handover.

  • 04

    Fluent in both rooms

    I can defend a design to staff engineers and explain the trade-off to the board.

02Expertise

Where I do my best work.

Six disciplines that show up in every engagement, sharpened across banking, betting, commerce, big tech, health tech and consulting.

01

Cloud Data Platforms

Architecture and build-out across all three hyperscalers — lakehouse foundations, governed warehouses, and the plumbing that keeps them cheap and fast.

  • GCP
  • AWS
  • Azure
  • Databricks
02

Streaming & Real-Time

Event-driven pipelines that stay correct under load, from Pub/Sub, Kinesis and Event Hubs through to sub-second analytics and alerting.

  • Dataflow
  • Pub/Sub
  • Kinesis
  • Event Hubs
03

Agentic AI & MLOps

Production ML and LLM systems on enterprise data — feature stores, Vertex AI endpoints, evaluation, guardrails and the CI/CD that makes them shippable.

  • Vertex AI
  • Agents
  • RAG
  • MLOps
04

Data Modeling & Warehousing

Dimensional and lakehouse modeling, warehouse migrations, and complex ETL structures built to survive a decade of changing requirements.

  • BigQuery
  • Redshift
  • Synapse
  • Snowflake
05

Platform Engineering

Infrastructure-as-code, CI/CD and cost control — Terraform, Kubernetes, Cloud Build and Composer — so platforms stay auditable and cheap in production.

  • Terraform
  • Kubernetes
  • CI/CD
  • FinOps
06

Leadership & Delivery

Building and mentoring engineering teams, owning technical oversight of partners, and acting as the bridge between C-suite stakeholders and delivery.

  • Team building
  • Mentoring
  • Stakeholders
  • Strategy
  • BigQuery
  • GCP Dataflow
  • Pub/Sub
  • Vertex AI
  • Databricks
  • Apache Spark
  • Apache Beam
  • Apache Airflow
  • Terraform
  • Kubernetes
  • Azure Data Factory
  • Azure Synapse
  • AWS Glue
  • AWS Kinesis
  • Amazon Redshift
  • Snowflake
  • Python
  • Scala
  • Java
  • SQL
  • MLOps
  • Agentic AI
  • CI/CD
  • BigQuery
  • GCP Dataflow
  • Pub/Sub
  • Vertex AI
  • Databricks
  • Apache Spark
  • Apache Beam
  • Apache Airflow
  • Terraform
  • Kubernetes
  • Azure Data Factory
  • Azure Synapse
  • AWS Glue
  • AWS Kinesis
  • Amazon Redshift
  • Snowflake
  • Python
  • Scala
  • Java
  • SQL
  • MLOps
  • Agentic AI
  • CI/CD
03Career journey

Twelve years, four countries, one throughline.

From data analyst to senior manager — banking, betting, commerce, big tech, health tech and consulting. Every step added a layer to how I build.

  1. PwC Canada

    Present
    2026 — Present
    • Senior Manager — Cloud, Data & AI EngineeringMarch 2026 — Present · Edmonton, Alberta
    • Spearhead end-to-end cloud, data and AI strategy for enterprise clients, aligning architecture with business objectives.
    • Lead engineering teams designing AI/ML models and data pipelines on Azure, AWS and GCP.
    • Advise senior leadership and clients, turning cloud and data concepts into ROI-driven roadmaps.
    • Raise data maturity through governance frameworks, CI/CD and cost-effective cloud management, while mentoring engineers and architects.

  2. Life360

    2026
    • Principal Data EngineerJanuary 2026 — March 2026 · Edmonton, Alberta
    • Designed large-scale streaming pipelines for high-volume location and event data on Kinesis, Kafka and Spark-style architectures, with low latency and strong data-quality guarantees.
    • Owned incident investigation and SLA-driven monitoring to restore production data flows quickly.
    • Enforced data contracts with external partners and built Python and SQL streaming transformations into analytics-ready lakes.
    • Orchestrated Airflow DAGs covering ingestion, validation and reporting against defined SLAs.

  3. MedWatch Technologies

    2025 — 2026
    • Manager, Data EngineeringJanuary 2025 — February 2026 · Edmonton, Alberta
    • Set the architectural vision for a HIPAA-compliant GCP lakehouse unifying wearable biosensing telemetry with clinical EHR data.
    • Directed Pub/Sub and Dataflow streaming for multimodal biosignals with sub-second processing for real-time metabolic alerts.
    • Built the MLOps foundation for non-invasive glucose models — feature stores, quality checks and Vertex AI CI/CD.
    • Instituted zero-trust security and encryption across GCP, with HIPAA, GDPR and FDA-ready data provenance, while mentoring cross-functional engineering squads.

  4. Deloitte

    2025 — 2026
    • Senior Data EngineerNovember 2025 — February 2026 · Edmonton, Alberta
    • Architected GCP pipelines for a financial-services client using Dataflow and Pub/Sub for high-throughput real-time and batch processing.
    • Integrated Vertex AI for a centralized model registry and real-time prediction endpoints.
    • Built Cloud Build CI/CD for Cloud Functions, scheduled with Cloud Scheduler, and Cloud Composer DAGs for auditable orchestration.

  5. ATB Financial

    2025
    • Senior Data EngineerFebruary 2025 — August 2025 · Edmonton, Alberta
    • Built batch and streaming ETL on Apache Beam and Dataflow, landing Pub/Sub financial transactions in BigQuery for near real-time reporting.
    • Automated Dev-Test-Prod releases with Cloud Build, Terraform and GitHub CI/CD.
    • Optimized BigQuery schemas and queries, with Cloud Monitoring that held 99.9% data availability.
    • Shipped a cost-optimization pipeline over GCS and BigQuery that cut storage and analytics spend by more than 35%.

  6. Government of Alberta

    2024 — 2025
    • Software Data EngineerDecember 2024 — February 2025 · Edmonton, Alberta
    • Implemented schema validation, outlier detection and spatial joins that cut downstream reporting defects against Alberta's TIER framework.
    • Authored developer documentation and a TDD playbook adopted by three other government analytics teams, shortening analyst onboarding by 25%.

  7. Google

    2021 — 2024
    • Data Engineering LeadFebruary 2023 — December 2024 · Waterloo, Canada
    • Senior Data EngineerSeptember 2021 — January 2023 · Warsaw, Poland
    • Three years and four months at Google across Warsaw and Waterloo. Named Best Engineer — Warsaw in 2024, with more than 25 peer and leadership awards.
    • As Data Engineering Lead, directed senior engineers on GCP data platforms and oversaw multi-million-dollar cloud migrations for Fortune 500 customers into BigQuery and Cloud Storage lakehouses.
    • Designed Dataflow and Pub/Sub pipelines processing billions of daily events, and optimized BigQuery partitioning, clustering and slot management for latency and cost.
    • Standardized Terraform and Cloud Composer blueprints with data quality, observability and RBAC. Built a GCS and BigQuery inventory tool that helped decommission unused resources and cut GCP spend by 30% in three months.

  8. TradeDepot

    2021
    • Engineering Manager, Data AnalyticsJanuary 2021 — September 2021
    • Led AWS-native ETL on Glue, Lambda and Step Functions processing over 2 TB a day, lifting analytics availability by 40%.
    • Built S3 data lakes with Parquet, partitioning and Glue Data Catalog for Athena and Redshift Spectrum.
    • Automated ingestion on Airflow (MWAA), cutting manual intervention by 90%, with KMS, IAM RBAC and VPC peering across accounts.
    • Migrated transactional data into Redshift and shipped a Kinesis fraud-detection pipeline that reduced fraud losses by more than 40% in the first six months.

  9. BetKing

    2019 — 2021
    • Manager, Data EngineeringJuly 2020 — January 2021
    • Data EngineerAugust 2019 — May 2020 · London Area, United Kingdom
    • Directed Azure Data Factory and Synapse pipelines unifying SQL Server, Blob Storage and REST APIs for executive reporting.
    • Built ADLS Gen2 with Delta Lake, plus Event Hubs and Stream Analytics for sub-second operational alerting.
    • Migrated the estate from on-prem SQL Server to Azure Data Lake and Azure SQL DW, cutting downtime 85%, accelerating BI readiness 70% and reducing infrastructure cost 30%.
    • Automated ETL and ML delivery with Azure DevOps, Databricks autoscaling and PySpark streaming, with Airflow DAGs and Key Vault, Private Endpoints and RBAC.

  10. Sterling Bank Plc

    2017 — 2019
    • Data EngineerJuly 2018 — August 2019
    • Business Intelligence DeveloperJanuary 2018 — July 2018
    • Application Support EngineerFebruary 2017 — December 2017
    • Led the migration of the on-prem warehouse to Azure with Databricks and SSIS, improving analytics availability by 90% and enabling self-service reporting.
    • Helped the AML team build fraud and anti-money-laundering detection systems.
    • Managed as much as 5 TB of BI data and shipped company-wide intelligence-sharing dashboards on the Azure stack.
    • Supported internet and mobile banking platforms and more than doubled e-channel onboarding through improved reliability.

  11. CrispTV

    2014 — 2017
    • Data AnalystNovember 2014 — January 2017
    • Built SSIS packages loading flat files, XML and Oracle into Azure SQL Data Warehouse, with error handling and logging.
    • Designed Power BI models, visuals and stakeholder dashboards, translating reporting needs into technical specifications.
04Digital twin

Ask my digital twin.

An AI trained on my CV, answering questions about my career in my own voice. Ask it what I actually did at Google, or whether I have shipped what you need.

IO

Digital Twin

Online · gpt-oss-120b

Hi, I'm Ikenna's digital twin. Ask me anything about his twelve years in data engineering — Google, PwC, the hyperscaler certifications, or the lakehouse and MLOps work.

AI generated from Ikenna's CV · May be imprecise · Verify anything that matters

05Credentials

Certified across the stack, formally trained for AI.

Certifications

  • Google Cloud

    • Professional Cloud Architect2022
    • Professional Data Engineer2022
    • Professional Cloud Developer
    • Professional Cloud DevOps Engineer2023
    • Professional Cloud Database Engineer2023
    • Associate Cloud Engineer2022
    • Cloud Digital Leader2023
    • Generative AI Leader

  • Amazon Web Services

    • AWS Certified Data Engineer2025
    • AWS Certified DevOps Engineer – Professional2023
    • AWS Certified Solutions Architect2022
    • AWS Certified Developer2023
    • AWS Certified Database – Specialty2023
    • AWS Certified Cloud Practitioner2022

  • Microsoft

    • MCSE: Data Management and Analytics2019
    • MCSA: SQL 2016 Database Development2019
    • Azure Data Fundamentals2024
    • Azure Fundamentals2024

  • Databricks

    • Databricks Certified Data Engineer2025

  • Neo4j

    • Neo4j Certified Professional2024

  • Simplilearn

    • Big Data for Data Engineering
    • Data Engineering with Hadoop

Education

Completed March 2024

Data ScienceTech Institute

Applied MSc, Data Engineering for Artificial Intelligence

Paris, France

Completed October 2014

Federal University of Technology, Owerri

BEng, Electronics and Computer Engineering

Nigeria

06Portfolio

Selected work, coming soon.

A set of deep-dive case studies on the platforms, pipelines and AI systems I have shipped. Currently being written up with the detail they deserve.

Portfolio
Case study

HIPAA lakehouse for wearable and EHR data

Write-up in progress

Case study

Real-time fraud detection on Kinesis

Write-up in progress

Case study

Fortune 500 GCP migrations

Write-up in progress

07Contact

Got a data or AI problem worth solving?

I take on a small number of advisory conversations, platform reviews and speaking engagements each year. If you are rebuilding a data foundation or putting agentic AI into production, get in touch.

Mountain Time · Replies within two days