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Designs and maintains scalable big data and cloud solutions using GCP, BigQuery, Python, PySpark, and Airflow. Develops data pipelines, APIs, serverless applications, and Kubernetes workloads; implements CI/CD and Terraform infrastructure; manages cloud networking, IAM, security, monitoring, and observability; troubleshoots production issues and improves system reliability and performance.

Design, build, deploy, and support production machine learning, AI, and GenAI solutions. Responsibilities include developing Databricks ML pipelines, AI agents, RAG applications, APIs, and microservices; deploying models on Azure Kubernetes Service; implementing MLOps, CI/CD, monitoring, governance, and observability; troubleshooting production systems; optimizing scalability and reliability; collaborating with data scientists and infrastructure teams; and mentoring junior engineers.

Leads integration engineering delivery, ensuring code quality, documentation, reliability, and adherence to project timelines. Provides hands-on technical guidance, unblocks developers, manages risks, reviews technical specifications against quality standards, and coordinates closely with management. Oversees daily execution, follows up on action items, communicates progress and challenges, escalates issues with proposed solutions, and contributes to development, maintenance, and support activities.

Leads integration architecture and delivery, ensuring code quality, documentation, reliability, and adherence to project timelines and client standards. Provides hands-on technical guidance, unblocks developers, identifies risks, tracks tasks, and communicates status and challenges to management. Supports build and maintenance activities, reviews technical specifications, and drives execution across technical teams. Workato experience is advantageous.

Manage data and infrastructure operations across SQL, CI/CD, cloud deployments, monitoring, incident management, and automation. Support reliable data pipelines and analytics platforms through observability, root cause analysis, capacity planning, and performance optimization. Collaborate with global engineering, product, infrastructure, security, and compliance teams. Use Python or Shell, Terraform, and Ansible to improve operational efficiency while participating in Agile delivery and a 24/5 rotational support model.

Develop and maintain MicroStrategy and Logi Analytics reports, dashboards, metrics, datasets, and data visualizations. Write and optimize advanced SQL, validate and reconcile data, analyze BI logic, and work with stakeholders to clarify requirements. Apply relational database, data warehousing, and dimensional modeling knowledge. Use AI-assisted development tools daily, establish responsible AI coding standards, and evaluate LLM-based workflow and automation tooling.

Develop and maintain production data transformation pipelines using dbt, SQL, and cloud data platforms. Build incremental models, macros, tests, snapshots, seeds, sources, documentation, and lineage. Apply data quality practices and establish standards for AI-assisted development using tools such as Claude Code and GitHub Copilot. Evaluate AI tooling, integrate agentic automation, and review AI-generated code for quality, security, and intellectual property compliance.

Develop and maintain production data transformation systems using dbt, SQL, and cloud data platforms. Build incremental models, macros, tests, snapshots, documentation, lineage, and data quality frameworks. Establish standards for AI-assisted development using Claude Code or GitHub Copilot, including prompt engineering, code review, security safeguards, and productivity measurement. Integrate AI into developer workflows and automation tooling while applying practical knowledge of LLM fundamentals.

Build and maintain production data transformation systems using dbt, SQL, and cloud data platforms. Develop models, incremental pipelines, macros, tests, snapshots, documentation, lineage, seeds, sources, and packages. Establish standards for AI-assisted development using Claude Code or GitHub Copilot, including prompting, code verification, security guardrails, and review practices. Integrate AI into developer workflows and automation tooling while evaluating productivity and quality outcomes.

Build and maintain production data transformations using dbt, SQL, and cloud data platforms. Develop models, incremental pipelines, macros, tests, snapshots, documentation, and lineage. Establish data quality practices and use AI-assisted development tools such as Claude Code or GitHub Copilot. Set standards for secure, reliable AI-generated code and integrate AI into developer workflows and automation.