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ChuksJoy/README.md

Hi, I’m Joy Chuks-Nwosu 👋

Senior Analytics Engineer | Microsoft Certified Fabric Data Engineer
SQL • dbt • PySpark • Airflow • Azure • Power BI • Kafka • Python
Designing trusted analytics layers, scalable transformations, and data products teams can rely on

I’m a Senior Analytics Engineer with a strong data engineering foundation and deep experience working at the intersection of data transformation, analytics, and governance.

I specialise in building analytics-ready data models, dbt-driven transformation layers, and reliable reporting foundations that power decision-making across banking, fintech, and enterprise environments.

I’ve worked across multi-country teams, subsidiaries, and group-level platforms, delivering governed, well-documented data products from ingestion to executive dashboards.

What I Focus On

  • Designing analytics engineering layers (staging → marts → metrics)
  • Building dbt models with testing, documentation, and lineage
  • Enabling self-service analytics through clean semantic layers
  • Partnering with analysts, engineers, and business stakeholders
  • Embedding data quality, governance, and privacy by design
  • Turning complex datasets into trusted, decision-ready assets

Core Stack

Analytics Engineering & Transformation

  • dbt
  • SQL (T-SQL, PL/SQL)
  • Python
  • Apache Spark (PySpark)

Dimensional modelling, analytics-ready marts, tested and documented transformations.

Orchestration & Pipelines

  • Apache Airflow
  • Azure Data Factory
  • SSIS

Incremental loads, dependency management, monitoring, and recovery.

Data Platforms & Storage

  • Microsoft Fabric / OneLake
  • Delta Lake
  • Snowflake
  • PostgreSQL
  • SQL Server

Lakehouse and warehouse architectures optimised for analytics consumption.

BI, Analytics & Consumption

  • Power BI
  • Tableau
  • SSRS

Semantic models, KPI definitions, and performance-optimised dashboards.

Engineering Practices

  • Git & GitHub
  • Docker
  • CI-friendly dbt projects
  • Clear documentation & handover

What I’ve Delivered

  • Built analytics transformation layers using dbt to serve multiple business units and group-level reporting.
  • Designed and maintained data marts feeding Power BI, SSRS, and Tableau across subsidiaries.
  • Engineered end-to-end pipelines from source systems to analytics consumption layers using Airflow, Spark, and Fabric.
  • Implemented data quality checks, anomaly detection, and testing frameworks to improve trust in KPIs.
  • Authored data dictionaries, lineage documentation, and transformation specs for analytics teams and auditors.
  • Led data literacy and quality programmes, training over 6,000 staff across regions and verticals.
  • Embedded governance, privacy, and compliance into analytics workflows (NDPA, CBN, GDPR-aligned).

Selected Projects

Enterprise Analytics Layer

Designed dbt-based transformation pipelines delivering analytics-ready tables for executive dashboards and regulatory reporting.

Data Quality Monitoring Framework

Built automated checks and anomaly detection across critical datasets, improving confidence in reporting.

Group-to-Subsidiary Reporting Pipelines

Engineered scheduled pipelines consolidating data from multiple subsidiaries into a central reporting platform.

Certifications & Professional Development

  • Microsoft Certified: Fabric Data Engineer Associate
  • Microsoft Certified: Power BI Data Analyst
  • DCAM Certified – EDMCouncil
  • Continuous learning in KSQL, dbt analytics engineering, Real-time data streaming and engineering, Kafka, Python Advanced Concepts, and privacy-by-design

Let’s Connect

Pinned Loading

  1. Azure-Data-Engineering-medallion-architecture Azure-Data-Engineering-medallion-architecture Public

    In this project, I built an industry-ready Azure data engineering pipeline from start to finish, applying modern cloud best practices and the Medallion Architecture (Bronze → Silver → Gold).

    Jupyter Notebook

  2. Azure-End-To-End-Data-Engineering-Project Azure-End-To-End-Data-Engineering-Project Public

    This project is an end-to-end data engineering and analytics pipeline

    Python

  3. End-to-End-Data-Engineering-Project-with-DBT-Spark-Azure-Cloud-Services End-to-End-Data-Engineering-Project-with-DBT-Spark-Azure-Cloud-Services Public

    This is an end to end data engineering using Apache Spark, Azure Databricks, Data Build Tool (DBT) using Azure as our cloud provider. This project illustrate the process of data ingestion to the la…

    Jupyter Notebook

  4. Financial-Fraud-Data-Analytics-Engineering Financial-Fraud-Data-Analytics-Engineering Public

    This project focuses on the exploratory analysis of a financial transactions dataset containing users, cards, and transaction records.

    Jupyter Notebook

  5. Google-Cloud-Realtime-Data-Streaming Google-Cloud-Realtime-Data-Streaming Public

    In this project, I am building an end-to-end real-time data engineering pipeline that captures live activity from YouTube videos or playlists and delivers instant notifications to Telegram whenever…

    Python

  6. Medallion-Architecture-with-Fabric Medallion-Architecture-with-Fabric Public

    This project demonstrates the implementation of a Medallion Architecture (Bronze, Silver, and Gold layers) using Microsoft Fabric Lakehouse and notebooks.

    Jupyter Notebook