Cloud Data Engineer - ETL & Analytics Pipelines

PETER ADEPOJU

Data Engineer Analytics Infrastructure Cloud Data Platforms

Arlington, TX 76001 682-***-**** *********@*****.*** linkedin.com/in/peteraadepoju

PROFESSIONAL SUMMARY

Data Engineer with 6+ years of experience designing, developing, and maintaining scalable data pipelines for multiple sources including APIs, SQL databases, and enterprise systems. Expert in Python (Pandas, NumPy, SQLAlchemy), SQL, AWS, Azure, and ETL development with proven ability to integrate complex datasets, automate data ingestion and transformation, ensure data quality and governance, optimize cloud infrastructure, and support advanced analytics initiatives through modern data engineering practices.

CORE COMPETENCIES

Data Engineering & Integration: Python (Pandas, NumPy, SQLAlchemy), SQL (complex queries, joins, CTEs, window functions), API integration, database design and management, ETL/ELT pipeline development, data transformation and validation, data versioning, time-stamping, workflow optimization, schema design

Cloud Platforms & Infrastructure: AWS (EC2, S3, Lambda, RDS, Glue, Redshift, IAM), Azure (Data Lake Storage, Databricks, Synapse, Virtual Machines, Identity Management), serverless computing, object storage, data integration services, query services, Infrastructure as Code (Terraform), cloud security and patching, disaster recovery, backups, code repository management

Data Quality & Governance: Data lineage documentation, metadata management, field definitions, data completeness and consistency monitoring, integrity validation, audit compliance, data governance frameworks, data quality standards, enterprise data management best practices

Analytics & Automation: ETL workflow automation, self-service analytical applications, dataset curation, reporting automation, process optimization, advanced analytics support, Databricks, Delta Lake, Agile/Scrum, Jira, Azure DevOps

PROFESSIONAL EXPERIENCE

Data Engineer December 2024 – Present

HealthEquity — Remote / Utah

Designed and developed scalable ETL pipelines integrating Qualtrics APIs, Salesforce enterprise systems, and digital telemetry databases into Databricks, automating data ingestion for 17M+ member accounts and reducing manual data pulls from weekly to daily cadence.

Built and maintained curated datasets using Python (Pandas, NumPy) and SQL, implementing data versioning and time-stamping protocols to ensure historical accuracy and support audit-ready analytics for leadership review.

Developed automated data validation and quality checks across multi-source integrations, monitoring data completeness and consistency between Qualtrics and Salesforce systems and documenting data lineage for audit compliance.

Engineered cloud infrastructure optimization through Databricks SQL query optimization and Power Automate workflow automation, improving pipeline reliability and reducing transformation processing time by 40%.

Maintained comprehensive metadata documentation and field definitions for curated datasets, supporting enterprise data governance requirements and enabling downstream analytics teams to understand available data assets and integration standards.

Collaborated with audit, compliance, and analytics teams to translate business requirements into technical data engineering specifications, providing guidance on available datasets and supporting continuous improvement initiatives across analytics functions.

Data Engineer July 2022 – October 2024

FTI Consulting — Remote / Texas

Designed and implemented data integration pipelines for 15+ client engagements across healthcare, financial services, retail, and energy sectors, integrating APIs, SQL databases, and enterprise systems to support analytics and audit functions.

Developed scalable ETL workflows using Python and SQL to transform disparate financial and operational data sources into curated datasets, reducing data preparation time by 60% and establishing reliable foundations for downstream analytics and reporting.

Built and maintained cloud-based analytics infrastructure supporting data ingestion, transformation, and validation processes, ensuring system availability, performance monitoring, and implementation of security best practices.

Implemented automated data quality frameworks with integrity validation and completeness checks across enterprise systems, documenting data lineage and governance standards for audit and compliance requirements exceeding 10 million records.

Mentored team members on data engineering best practices, ETL optimization techniques, and modern data pipeline architectures, supporting knowledge sharing through technical documentation and training sessions.

Evaluated emerging data technologies and cloud services to enhance analytics capabilities, including serverless computing, object storage optimization, and query service improvements for client engagements.

Data Engineer April 2020 – June 2022

FedEx — Remote / California

Designed and maintained ETL pipelines integrating high-volume package tracking, routing, and SLA data from multiple enterprise databases using SQL and Python, automating transformation and validation processes to support operational analytics.

Developed automated data quality validation workflows that monitored data completeness and consistency across regional hub systems, reducing data anomalies by 25% and improving reliability of downstream operational dashboards.

Built curated datasets and self-service analytical solutions for operations teams using Python (Pandas) and SQL, automating reporting and reducing manual data preparation effort by 45% and enabling faster data-driven decision making.

Optimized cloud data workflows and infrastructure performance, identifying bottlenecks in data pipelines and implementing SQL query optimization and transformation improvements that accelerated analytics delivery from days to hours.

Standardized data integration processes and metadata documentation across facilities through Infrastructure as Code principles, creating reusable pipeline templates and establishing enterprise data management standards that improved consistency and auditability.

EDUCATION

M.Sc. in Data Science University of Salford, England 2024

CERTIFICATIONS

Databricks Certified Data Analyst Associate

Google Data Analytics Professional Certificate

Oracle Cloud Infrastructure 2025 Certified AI Foundations Associate

Machine Learning with Python: Foundations

Data Analytics Essentials with Power BI

Build Dynamic, Interactive Microsoft Excel Dashboards

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