Building the pipelines that transform complex, raw data into reliable, governed assets, empowering strategic decision-making and compelling visual narratives.
Tech Stack:
Building the future of data-driven decision making
I'm Emmanuel Justice, a Data Engineer with over 5 years of professional experience specializing in building scalable data pipelines, architecting modern data stacks, and transforming raw data into strategic business assets. My expertise spans the full data lifecycle—from ingestion and transformation to warehouse design and analytics enablement.
With a strong foundation in Data Analytics, I bring a unique perspective that bridges the gap between engineering and business intelligence. I don't just build data infrastructure; I architect systems that empower data teams and drive measurable business outcomes through reliable, governed, and accessible data.
My technical toolkit includes cloud platforms (AWS, GCP, Azure), modern data orchestration tools (Airflow, dbt), data warehousing solutions (Snowflake, BigQuery), and a wide range of ETL/ELT frameworks. I'm passionate about implementing best practices in data modeling, pipeline automation, and data quality management.
Building production-grade data infrastructure
Transforming data into actionable insights
Engineering Clarity: Bridging Technical Complexity with Business Strategy.
I'm driven by the challenge of turning chaotic, messy data into well-organized, reliable systems that teams can depend on. There's something deeply satisfying about building a pipeline that runs smoothly, transforms data efficiently, and delivers exactly what stakeholders need when they need it.
My background in analytics gives me a unique advantage: I understand the end user. I know what makes a good data model, what queries analysts will run, and how to structure data for optimal performance and usability. This perspective ensures that every data platform I build isn't just technically sound it's practically useful.
I'm passionate about data engineering best practices: clean code, comprehensive testing, thorough documentation, and automation. I believe data infrastructure should be treated with the same rigor as any production software system, and I bring that mindset to every project I work on.
Currently seeking opportunities to contribute to innovative data teams building the next generation of data-driven products and platforms.
Explore my work in Data Analytics or Data Engineering
Explore dashboards, reports, A/B testing, and SQL insights. View projects using Power BI, Tableau, Python, and advanced analytics techniques.
Discover ETL/ELT pipelines, cloud infrastructure (AWS/Azure/GCP), data processing, and data warehouse architecture projects.
Insights and reporting driven by SQL, Python, Power BI, and Tableau
This project focuses on using machine learning techniques within Microsoft Excel to predict student academic outcomes based on various contributing factors. By leveraging data analytics and predictive modeling, the aim is to identify key variables that significantly impact student grades.
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Exploration of pizza order and delivery data using SQL queries to understand customer behavior and delivery performance.
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Analyzing customer sentiment on Amazon using MongoDB for storage, extracting insights from reviews and feedback.
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Leveraging SQL's querying capabilities to analyze public COVID-19 datasets and understand the global impact.
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Using data-driven insights to uncover patterns and factors contributing to employee turnover.
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Comprehensive exploration of sales performance aimed at uncovering actionable insights.
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Applying data analytics techniques to extract valuable insights from sales datasets.
View Project →ETL/ELT pipelines, data warehousing, and cloud technologies
An end-to-end data engineering platform orchestrating Airflow, Snowflake, dbt, and Docker on AWS EC2. Features hybrid ingestion (Structured + NoSQL) and real-time analytics.
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Designed and implemented an end-to-end Modern Data Stack for Pulse360 Media, consolidating content performance from YouTube, PipeDrive CRM, and Newsletters into a Snowflake data warehouse. Utilized dbt for transformations and Power BI for actionable dashboards, reversing engagement decline with data-driven content strategies.
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A simulated subscription data project demonstrating data extraction, transformation, and reporting for business insights.
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Built a scalable, serverless ETL data pipeline entirely on AWS, utilizing S3 for data lake storage, Lambda for event-driven, serverless transformations, and Airflow (on EC2) for robust workflow orchestration. This project showcases proficiency in designing automated, cloud-native data systems and managing complex task dependencies (DAGs).
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Tackled retail data silos by building a modern data stack. Leveraged Snowflake for scalable data warehousing, dbt for robust data modeling and transformation, and Git for version control. Transformed raw sales, inventory, and customer data from online and physical stores into clear, actionable dashboards for strategic business decision-making.
View Project →I'm currently building exciting data engineering projects featuring Spark, Apache Kafka, Airflow, AWS, and modern data pipeline architectures.
I'm currently seeking new data engineering opportunities!