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

As Manager – Data Engineering, I lead the operations and evolution of Inspire Brands’ Enterprise Data Platform—an ecosystem of 700+ 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: 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

• Reduced job failure rates through robust retry logic, standardized error-handling patterns, and conditional handling across Airflow & ADF.

• Led migration of ETL workflows from Databricks (PySpark) to Snowflake (SnowSQL), integrating orchestration into Airflow for unified monitoring and retries.

• Designed and deployed centralized Power BI observability dashboards for Airflow DAGs, ADF pipelines, REST APIs, SLAs, and data validations.

• Delivered real-time data lineage and job status visualizations, accelerating issue identification and preventing SLA breaches.

• Standardized DQ checks and validation alerts across pipelines, improving trust and consistency in enterprise data.

• Redesigned alerting logic to eliminate false positives and significantly reduce incident volume.

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.

Responsibilities

• Operations Management, Technical Support, Troubleshooting & Incident Resolution

  • Manage end-to-end data operations.

  • Drove the shift from a reactive support culture to a proactive, prevention-first approach.

  • Provide L2/L3 support for Data Platform, optimize performance and error recovery mechanisms.

  • Leveraged CoPilot to enhance support efficiency—using it for rapid code generation, knowledge retrieval, and guided troubleshooting to shorten resolution cycles.

  • Lead incident triage, RCA, and recovery for high-impact issues.

  • Developed troubleshooting playbooks and collaborated with Cross-Functional teams to resolve platform issues.

• Development, Enhancements & Production Fixes | Data Quality & Observability

  • Own sprint planning, backlog prioritization, and resource allocation across operations, enhancements, and production fixes.

  • Oversee design and deployment of new pipelines, enhancements; lead fixes for production issues.

  • Implement robust DQ checks, schema validations, and real-time anomaly detection across pipelines.

  • Built centralized observability dashboards (Power BI) and end-to-end lineage tracking.

• Team Leadership & Management | Collaboration & Stakeholder Management

  • Mentor and lead a high-performing team of data engineers, fostering a collaborative, growth-driven culture.

  • Conduct performance reviews, support hiring, and ensure technical skill development within the team.

  • Liaise with data scientists, engineers, and business teams to define requirements, deliver solutions, and enforce data engineering best practices.

• Process Improvement & Automation | Technology Adoption & Continuous Learning

  • Identify automation opportunities to reduce manual effort and improve operational efficiency.

  • Set up proactive monitoring, alerting, and self-healing mechanisms for critical pipelines.

  • Stay abreast of evolving trends in data engineering, cloud platforms, and automation tools.