Mark - Senior Databricks Data Engineer

Proficient in AWS for cloud platforms tasks

Proficient in Microsoft Azure for cloud platforms tasks

Proficient in Google Cloud Platform for cloud platforms tasks

Proficient in Python for programming & scripting tasks

Proficient in SQL for programming & scripting tasks

Implemented enterprise-scale Dynatrace, APM, Infrastructure Monitoring, and Observability solutions across distributed cloud-native government applications improving operational visibility, application diagnostics, and proactive issue detection for critical production services., Led full lifecycle Dynatrace Deployments, OneAgent rollout activities, ActiveGate integrations, and SmartStack optimization efforts supporting scalable monitoring adoption across hybrid cloud and containerized application environments., Built automated Monitoring Automation, Python, REST APIs, and CI/CD Pipelines workflows streamlining monitoring configuration management and reducing manual operational overhead across enterprise observability implementations., Engineered scalable Kubernetes Monitoring, Container Monitoring, Cloud Observability, and Microservices visibility capabilities supporting EKS, AKS, GKE, and OpenShift orchestration environments with centralized monitoring governance., Implemented enterprise monitoring and observability solutions across AWS, Microsoft Azure, and Google Cloud Platform (GCP), providing centralized visibility into cloud-native applications and distributed workloads., Supported AWS data and cloud workloads across Amazon EC2, Amazon S3, Amazon EKS, and CloudWatch, improving monitoring visibility, operational reliability, and production support across distributed enterprise environments., Implemented AWS CloudWatch monitoring, alerting, logging, and operational dashboards for cloud-hosted applications and data workloads, enabling proactive issue detection and faster troubleshooting., Collaborated with engineering teams supporting AWS-based containerized workloads on EKS and cloud infrastructure, strengthening observability, performance monitoring, and operational readiness., Supported Microsoft Azure workloads across Azure Virtual Machines, AKS, Azure Monitor, and cloud-native services, improving infrastructure visibility and application monitoring across enterprise environments., Implemented Azure Monitor dashboards, alerts, metrics, and operational monitoring for Azure-hosted applications and distributed workloads, improving incident detection and production support., Collaborated with Azure engineering teams to monitor AKS clusters, virtual machines, and application dependencies while strengthening cloud observability and operational governance., Integrated enterprise ITIL Processes, Incident Management, Change Management, and Release Management standards supporting compliant monitoring operations and structured production deployment governance across engineering teams., Automated observability operations using PowerShell, Bash, Python, and Automation Scripts improving monitoring deployment consistency and reducing environment configuration drift across enterprise systems., Collaborated with cloud engineering teams to monitor workloads running on Amazon EC2, Azure Virtual Machines, Google Compute Engine, Kubernetes clusters, and cloud-native managed services across multi-cloud environments., Enhanced cloud-native Observability Engineering, Application Monitoring, Infrastructure Visibility, and Monitoring Governance capabilities improving stakeholder reporting accuracy and operational transparency across production services., Supported enterprise Cluster Management, Dynatrace APIs, License Management, and Monitoring Standards initiatives ensuring scalable observability adoption and centralized monitoring administration across multiple environments., Supported Google Cloud workloads across Google Compute Engine, GKE, and Google Cloud Operations Suite, providing centralized monitoring and operational visibility across distributed applications., Implemented Google Cloud Operations monitoring, logging, alerting, and performance visibility for GCP-hosted workloads, improving proactive issue detection and troubleshooting., Collaborated with cloud engineering teams supporting GKE and Google Cloud infrastructure, strengthening observability, dependency monitoring, and production reliability across GCP environments., Built centralized Operational Metrics, Enterprise Reporting, Monitoring Dashboards, and Infrastructure Monitoring capabilities supporting executive visibility into enterprise application and infrastructure performance., Enhanced enterprise Container Observability, Cloud Native Monitoring, Dynatrace OneAgent, and Monitoring Governance implementations improving monitoring consistency and reducing operational blind spots across Kubernetes environments.

Designed scalable retail data pipelines using Azure Data Factory, Snowflake, and SQL to support sales and inventory analytics applying robust transformation logic for trusted reporting and using Python for automation across business functions., Developed cloud-based ingestion pipelines integrating data from Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage (GCS) into Snowflake and Azure Synapse Analytics for enterprise reporting., Built Incremental Ingestion Workflows using Azure Data Lake Storage landing patterns improving data freshness, reducing rework through partition-aware processing, and handling high-volume feeds that met hourly and daily business SLAs., Built scalable ELT workflows supporting Amazon Redshift, Google BigQuery, and Azure Synapse Analytics enabling high-performance analytical reporting across multiple cloud platforms., Built AWS data ingestion workflows using Amazon S3 and AWS Glue to process retail sales, inventory, product, and operational datasets for downstream analytics and reporting., Developed scalable analytical data pipelines integrating Amazon S3 and Amazon Redshift with Snowflake, supporting reliable ingestion, transformation, and enterprise reporting workloads., Implemented AWS CloudWatch monitoring and operational alerting for cloud-based data pipelines, improving visibility into failures, processing delays, and production workflow health., Developed Azure data pipelines using Azure Data Factory and ADLS Gen2 to ingest, transform, and organize high-volume retail datasets for enterprise analytics., Integrated Azure Synapse Analytics and Azure Databricks with cloud data lake workloads, supporting scalable transformation, dimensional modeling, and analytical reporting., Implemented Azure Monitor and operational monitoring for Azure-based data workflows, improving pipeline reliability, alerting, and proactive issue resolution., Built GCP data ingestion workflows using Google Cloud Storage and Google BigQuery to support scalable retail analytics and enterprise reporting requirements., Integrated Google Cloud Storage, BigQuery, and cloud-native processing services into multi-cloud data pipelines, improving data availability and analytical performance., Implemented Google Cloud Operations Suite monitoring for GCP data workloads, improving operational visibility, alerting, and proactive pipeline issue detection., Added operational visibility with proactive Monitoring, structured Alerting, stronger observability, and documented Runbooks simplifying triage for failed retail workflows and improving support readiness., Implemented cloud monitoring and operational dashboards using Azure Monitor, AWS CloudWatch, and Google Cloud Operations Suite to improve pipeline reliability and proactive incident detection., Built BI-friendly datasets with performant Aggregations supporting downstream Power BI use cases, reusable views, and improved analyst productivity across common retail reporting patterns., Automated Source-to-Target Reconciliation where row counts reduced manual verification, variance-focused checksums improved trust, and downstream data accuracy became easier to support., Enabled Omnichannel Reporting by integrating multiple data sources, creating governed domain-level consistency, improving unified business definitions, and delivering more trusted curated data across sales, product, and customer analytics., Implemented Schema Drift Detection that flagged breaking upstream changes, strengthened pre-load validation gates, and improved downstream pipeline reliability for evolving retail feeds., Reduced operational effort by designing Idempotent Processing Logic, strengthening workflow retries, improving exception-based Error Handling, and increasing end-to-end resilience for recurring data refresh cycles., Improved compute efficiency by aligning Cost Optimization with workload design, right-sizing warehouse resources, and replacing wasteful reload patterns with smarter Incremental Processing strategies., Partnered with stakeholders to translate requirements into reusable Data Products, support agile delivery, and ensure curated retail datasets aligned with business priorities and reporting expectations., Implemented rule-based controls where critical business Validation Logic caught failures early, exception tables improved triage, and support operations gained clearer visibility into Data Quality issues., Built conformed retail entities through strong Customer Analytics Design, aligned shared Dimensions, improved segmentation consistency, and reduced conflicting interpretations in downstream reporting., Tuned workflow sequencing by improving Dependency Management, reducing end-to-end batch duration, and improving downstream data freshness for business reporting consumers.

Built enterprise ETL Workflows using Informatica to move data across systems applying strong SQL transformation logic, improving operational performance, and increasing downstream reliability for reporting and warehouse delivery needs., Improved pipeline efficiency by identifying bottlenecks through Performance Tuning, refining workflow optimization, reducing unnecessary resource consumption, and increasing delivery speed across recurring database and ETL processes., Supported cloud-hosted data workloads across AWS, Microsoft Azure, and Google Cloud Platform (GCP) using Amazon EC2, Amazon S3, Azure Storage, Azure Virtual Machines, Google Cloud Storage (GCS), and Compute Engine, improving scalability and infrastructure reliability., Worked with provider teams to resolve upstream Data Quality issues, refine source schemas, improve integration compatibility, and strengthen downstream consistency across ingestion and transformation workflows., Implemented CloudWatch monitoring with proactive operational alerts and structured infrastructure dashboards improving observability for cloud-hosted systems and data processing services., Built automated ETL workflows using AWS Glue, Azure Data Factory, and Google Cloud Dataflow to support enterprise data integration and cloud migration initiatives., Developed AWS ETL workflows using Amazon S3, AWS Glue, and Amazon EC2 to support enterprise data ingestion, transformation, and cloud migration initiatives., Supported AWS-based data processing workloads by integrating S3 storage with ETL workflows, improving scalability and reliability for recurring enterprise data pipelines., Implemented AWS CloudWatch monitoring and operational alerts for cloud-hosted ETL workloads, improving visibility into infrastructure and pipeline execution issues., Developed Azure ETL pipelines using Azure Data Factory, Azure Storage, and Azure Virtual Machines to support enterprise data integration and migration workloads., Assisted with Azure cloud migration initiatives by moving on-premises data workloads into Azure Storage and cloud-based processing environments while improving scalability., Supported Azure-based data workloads through monitoring, workflow troubleshooting, and operational optimization across enterprise ETL environments., Developed GCP data pipelines using Google Cloud Storage, Compute Engine, and Google Cloud Dataflow to support enterprise ingestion and transformation workloads., Assisted with GCP cloud migration activities by moving data workloads into Google Cloud Storage and cloud-based processing environments., Supported Google Cloud data processing workloads through pipeline troubleshooting, performance optimization, and operational monitoring across enterprise environments.