Driving the data platform design & operations engine that powers insights, decisions, and the business itself.

This phase is where everything gains velocity. From frontline support to cloud and data engineering, I now lead the operational heartbeat of Inspire Brands’ Enterprise Data Platform—fueling real-time transactions, customer intelligence, loyalty insights, marketing activations, and core business operations.

I manage a high-performing team of data engineers and own the reliability, scalability. Design and development of 100+ data pipelines and day-to-day performance of 750+ production pipelines across ADF, Databricks, Snowflake, ADLS, Airflow, and multi-cloud data sources.

I also leverage Azure OpenAI and Copilot to embed AI into data operations—powering intelligent automation, accelerating root-cause analysis, and generating actionable insights that improve platform reliability and operational efficiency.

My work sits at the center of data platform: transforming complex data into meaningful insights, directing critical incidents, strengthening platform resilience, modernizing workflows, enforcing data quality, and driving automation that removes friction from the business.

This chapter marks the shift from leading teams to leading the entire data platform —the pipelines, platforms, and processes that keep the organization running end to end.

The Present: Leading the Data Operations That Run the Enterprise

Manager – Data Engineering | Inspire Brands | Oct’23 – Present

As Manager – Data Engineering, I lead the design, development and operations of Inspire Brands’ Enterprise Data Platform—an ecosystem of 750+ high-stakes pipelines powering transactions, customer intelligence, loyalty, campaigns, and operational insights across the business.

I guide a high-performing team of engineers and own the stability, scalability, and performance of a deeply complex modern data stack spanning ADF, Databricks, Snowflake, Airflow, ADLS, and multi-cloud sources.

I also leverage Azure OpenAI and Copilot to embed AI into data operations—powering intelligent automation, accelerating root-cause analysis, and generating actionable insights that improve platform reliability and operational efficiency.

My work blends decisive leadership with hands-on technical oversight: designing scalable & reliable data pipelines, critical incident management, platform hardening, architectural modernization, automation, and data quality governance.

This role is where I cement the engineering and operational rigor required to run a large-scale, cloud-native data platform that the business depends on every single day.

Highlights

  • Designed 100+ and managed 750+ data pipelines across enterprise data platforms

  • Reduced incident volume by 30–40% through proactive monitoring and automation

  • Migrated 300+ legacy ETL pipelines to modern cloud-native architecture (Azure & Snowflake)

  • Built advanced observability dashboards enabling real-time anomaly detection and faster root-cause analysis

  • Recognized with Innovation Awards for AI-driven automation and centralized monitoring solutions

Takeaways & Learnings

This role made me what I am, I evolved into a modern data platform leader—driving reliability, quality, and innovation across a complex, cloud-scale engineering ecosystem.

  • Built deep expertise in operating and scaling a modern, cloud-native data platform across ADF, Snowflake, Databricks, ADLS, and Airflow.

  • Strengthened leadership capability by managing high-stakes operations, critical incidents, and cross-team engineering efforts.

  • Developed a product-mindset toward pipelines—focusing on reliability, observability, automation, and continuous improvement.

  • Improved architectural thinking through large-scale migrations, workflow redesigns, and platform-wide reliability patterns.

  • Sharpened decision-making under pressure, balancing business impact with technical constraints.

  • Learned to build a high-performing team culture centered on accountability, ownership, and technical excellence.

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Responsibilities

  • Designing and building scalable data platforms using Azure Data Factory, Databricks, Airflow, Python, and Snowflake—enabling reliable data ingestion, transformation, and analytics at scale.

  • Architecting AI-driven automation solutions using Azure OpenAI and Microsoft Copilot to power intelligent monitoring, anomaly detection, and self-healing data pipelines—reducing issue resolution time from ~1 hour to under 10 minutes.

  • Implementing robust data quality frameworks including schema validation, automated checks, and anomaly detection, along with centralized observability dashboards in Power BI for end-to-end pipeline visibility and data lineage.

  • Driving DataOps practices across pipeline design, deployment, monitoring, and performance optimization—ensuring high availability, reliability, and operational efficiency of enterprise data platforms.

  • Leading and mentoring high-performing data engineering teams, managing delivery through agile practices, and collaborating with cross-functional stakeholders to build scalable, business-aligned data solutions.

  • Continuously improving platforms through automation, proactive monitoring, and adoption of emerging technologies in cloud data engineering and AI.

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