Lead Data Platform Engineer (GCP)
Resume:
Elijah Smith Los Angeles, CA
*************@*******.***
linkedin.com/in/elijah-smith-engineer
Professional Summary
Lead engineer and data platform specialist with 10+ years of experience delivering production software, data pipelines, and cloud infrastructure across digital health, regulated financial services, and education technology. Combined strong backend engineering with hands-on GCP data stack expertise including BigQuery, Dataflow, Cloud Composer, Pub/Sub, and Dataproc to move data initiatives from discovery through production deployment and support. Built scalable data processing workflows, automated infrastructure and release pipelines, established observability and governance practices, and translated complex technical requirements into decisions that product, operations, and engineering stakeholders could act on. Worked as a consultative technical lead across solution design, implementation, testing, deployment, and production support, balancing platform performance with maintainability, security, cost, reliability, and business value. Known for pragmatic problem solving, production-quality engineering, clear communication with technical and non-technical audiences, and mentoring teams on repeatable data delivery practices. Work History
Senior Software Engineer / Data Platform Architect Huma, London, UK May 2021 - Present
• Led the design and delivery of production-ready data workflows and internal tooling that made the platform easier to use day to day, with a strong focus on reliability, observability, and operational clarity so teams could trust what the product was doing and move faster without creating incidents.
• Drove performance improvements and operational clarity across distributed services, using telemetry-driven debugging and monitoring to trace failure paths and isolate regressions before release, keeping releases calm even as the system grew.
• Built user-facing workflows and internal data tooling that connected REST APIs and data pipelines, enabling teams to access and act on platform data without manual intervention, reducing repeated support issues and improving daily operational efficiency.
• Pushed hard on data governance and security practices across the platform, ensuring that data access, transformation, and storage followed consistent standards and that sensitive information was handled correctly throughout the data lifecycle.
• Collaborated with product, operations, and engineering stakeholders to take ambiguous product ideas and turn them into shipped, production-ready features, acting as a consultative technical lead across solution design, estimation, implementation, testing, and deployment.
• Established CI/CD practices and automated release workflows that reduced friction in shipping and maintaining the product, enabling the team to deploy changes with greater confidence and fewer production incidents.
• Mentored engineers through code review and pair programming, raising team quality and building repeatable engineering practices around testing, documentation, and production support.
• Worked extensively with Python and SQL to build data processing logic, troubleshoot production issues, and optimize query performance across large datasets, ensuring predictable behavior under real-world usage at scale.
• Designed and implemented internal tooling that automated manual operational steps, connecting external services and modernizing data handling so the system responded faster and was easier to maintain.
• Led end-to-end delivery on multiple cross-functional initiatives, coordinating with stakeholders and making sure complex data flows remained predictable in production, balancing platform performance with maintainability and business value.
• Introduced practical improvements to the platform's data architecture, including cleaning up slow or fragile workflows and modernizing data handling patterns, which improved system responsiveness and reduced operational overhead.
• Acted as a trusted technical advisor to leadership, articulating the ROI of platform investments and managing technical risk across data and infrastructure initiatives. Software Engineer / Data Engineer
Capital One, McLean, VA May 2017 - May 2021
• Built workflow-heavy systems where correctness, security, and edge cases were not optional, treating every feature like it needed to survive real-world usage at scale in a regulated financial services environment.
• Drove end-to-end delivery on multiple data and workflow initiatives, coordinating with stakeholders across product, operations, and risk teams to make sure complex flows remained predictable in production and met data governance requirements.
• Implemented data processing and transformation logic using Python and SQL, handling large volumes of transactional and operational data while maintaining strict data quality and security standards required in financial services.
• Raised team quality through mentoring and code review, establishing repeatable engineering practices around testing, documentation, and production support that reduced friction in shipping and maintaining the product.
• Designed and built automated workflows and internal tooling that removed manual steps from operations, making the team more efficient and reducing repeated support issues across multiple business lines.
• Collaborated with cross-functional teams in an Agile environment to deliver features that met strict regulatory and security requirements, ensuring that data handling and access controls were consistently applied across all systems.
• Improved system observability and monitoring capabilities, enabling faster incident response and more confident production deployments across complex, interdependent services.
• Consistently looked for practical improvements that reduced friction in shipping and maintaining the product, including CI/CD pipeline enhancements and automated testing coverage that caught regressions before they reached production.
• Worked with relational databases and data warehousing concepts to support reporting and analytics needs, optimizing queries and data models for performance and maintainability. Software Engineer
Kaplan, Fort Lauderdale, FL Nov 2015 - May 2017
• Worked across the product surface area, building core experiences and internal tools that teams used every day to run programs and support users, with a focus on data handling and workflow automation.
• Took ownership of improving legacy areas by cleaning up slow or fragile workflows and modernizing data handling so the system responded faster and was easier to maintain, reducing operational overhead and support burden.
• Connected external services through API integrations to remove manual steps from operations, which made the team more efficient and reduced repeated support issues across program management and user support functions.
• Built and maintained data processing logic and reporting tools that gave teams visibility into program performance and user activity, supporting data-driven decision making across the organization.
• Collaborated with product and operations stakeholders to deliver features that improved daily workflows, ensuring that data was accurate, accessible, and actionable for end users.
• Implemented automated testing and CI/CD practices that improved release reliability and reduced the time required to ship new features and fixes.
• Worked with SQL and relational databases to support data storage, retrieval, and reporting needs, optimizing queries and data models for performance and maintainability.
• Participated in code review and mentoring activities, helping to raise team quality and establish consistent engineering standards across the codebase. Skills
• Data Architecture & Warehousing: BigQuery, BigLake, Omni, Google Cloud Storage, Data Modeling, Data Warehousing, Data Governance, Dataplex, Data Masking, Encryption, IAM, VPC Service Controls, Data Readiness Placement, Self-Serve Data Platforms
• Data Processing & Orchestration: Dataflow, Apache Beam, Dataproc, Spark, Hadoop, Cloud Composer, Airflow, dbt, Pub/Sub, Confluent, Kafka, ETL, ELT, Batch Processing, Stream Processing
• Analytics & AI: Looker, Vertex AI, BigQuery ML, Business Intelligence, Data Visualization, Predictive Analytics, Machine Learning Pipelines
• Infrastructure & DevOps: Terraform, Pulumi, GKE, Kubernetes, Docker, CI/CD, Agile, DevOps, Infrastructure as Code, Version Control, Automated Testing, Observability, Monitoring, Incident Response
• Programming & Databases: Python, Java, SQL, Advanced SQL, REST APIs, PostgreSQL, Relational Databases, NoSQL, Data Migration, Performance Tuning, Query Optimization
• Leadership & Delivery: Cross-Functional Team Leadership, Technical Mentoring, Code Review, Stakeholder Management, Executive Communication, Solution Architecture, Roadmap Planning, Technical Risk Management, Agile Delivery
Education
Master’s Degree, Computer Science
University of Southern California
2011 - 2015
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