Takashi Koike

Data Engineer with extensive experience in designing, automating, and optimizing data pipelines across Azure, GCP, and hybrid cloud environments. Skilled in data modeling, integration, cleansing, performance tuning, and building scalable ETL/ELT solutions. Proven track record of leading end-to-end data initiatives and BI reporting across industries including retail, banking, insurance, marketing, and supply chain.

• Designed and automated scalable data extraction and transformation pipelines in cloud environments (Azure, GCP), incorporating data cleansing, validation, and QA scripting to ensure high integrity and performance.
• Implemented automated data profiling and monitoring solutions to validate business rules, detect anomalies, and trigger resolution workflows across cloud-based data lakes.
• Assessed and optimized pilot projects and cloud-native proof-of-concepts (e.g., Databricks Auto Loader, Delta Lake), resolving performance bottlenecks and recommending architecture improvements.
• Established best practices for cloud-based enterprise data solutions—covering pipeline orchestration, performance tuning, CI/CD integration, and cost-effective maintenance strategies.
• Built end-to-end BI dashboards with cloud-based tools (Power BI, Looker, or Databricks SQL) to track pipeline health, update latency, and storage optimization metrics.
• Collaborated with cloud architects and infrastructure teams to integrate diverse data sources into Azure Data Lake, GCP Big Query, and hybrid architectures using modern ingestion tools and cataloging systems.
• Provided production support and incident resolution in cloud environments, including Spark tuning, job reruns, failure diagnosis, and performance refactoring.
• Led cloud-based data acquisition and ingestion initiatives, developed quality assurance strategies, and contributed to data model governance in line with enterprise standards.
• Acted as a liaison with business teams to address cloud data pipeline challenges including data latency, schema drift, and change data capture (CDC) in real time.
• Collected business and system-level requirements and translated them into cloud data lake architecture, schema design, and scalable modeling solutions.
• Coordinated project teams spanning cloud engineers, data analysts, and reporting developers to deliver integrated solutions aligned with enterprise goals.
• Mentored junior team members on efficient Spark/PySpark coding, query optimization, and cloud-based pipeline performance monitoring and cost controls.
• Authored cloud-centric technical documentation, including schema diagrams, data flow charts, validation test plans, and infrastructure runbooks.

Data Analysis/SAS Programming Cert. Metro College

Diploma. Software Development (Honors) Red River College

BSc. Mathematics/Statistics University of Winnipeg