About
Michael Shawn Lockwood — Data Engineer & DBA
As an Application Database Developer and Data Platform Engineer, I’ve spent my career operating at the intersection of SQL Server development and database administration—designing performant T-SQL while ensuring stability, scalability, and operational readiness in live environments. As a Microsoft Certified Database Administrator, I’ve worked as the sole DBA in multiple organizations, acting as the guardrail between application changes and production performance. My focus has always been solving issues at the data layer rather than compensating with infrastructure.
At OnePoint Patient Care, I served as Director of Information Infrastructure for more than 5 years, where I designed and built the full SQL Server data environment supporting the OnePointRx healthcare application. I also assembled and managed the application development team responsible for building OnePointRx. This included integrating HL7 clinical data, managing PHI securely, and ensuring data handling aligned with HIPAA requirements in a production environment.
I approach data platforms with a performance-first mindset—grounded in two decades of SQL Server engineering and extended into modern data architectures. I specialize in performance, scalability, and automation engineering across the full data lifecycle. My approach to database performance is rooted in pattern recognition, root cause analysis, continuous baselining, bottleneck identification, early anomaly detection, and workload-aware tuning.
I’m a naturally curious professional who takes pride in understanding how every layer of data and software architecture fits together. I approach challenges with a performance-first, automation-second mindset. I value precision, reliability, and transparency—traits that have defined my success across healthcare, finance, and hospitality systems for more than two decades.
I began database development using Microsoft Access while in college and transitioned to SQL Server early in my career, working with Data Transformation Services (DTS). I earned my Microsoft Certified Database Administrator (MCDBA) certification in 2006. SQL Server, T-SQL, and SSIS form the foundation of my career. I also hold a Bachelor’s degree in Business Administration from DeVry University in 2003.
Most recently, I designed and built a full-stack workflow orchestration, bookkeeping, and data administration platform using React/TypeScript, ASP.NET/C#, PostgreSQL, Vault, MinIO, and TLS-secured internal services on Debian (Bookworm). I implemented per-tenant database isolation by securing connection strings as Vault-managed secrets, along with server-side rendering, PDF generation, and automated workflow execution driven by React Flow and JSON-based models. The platform is a production-grade business data management solution incorporating agentic AI patterns for workflow orchestration and automation.
Additionally, I developed a large-scale data pipeline spanning SQL Server and Databricks, integrating PolyBase, Parquet datasets, and S3-compatible storage (MinIO/Azurite). This work focused on efficient data ingestion, query performance, and analytical workflows, with visualization delivered through Power BI and Databricks dashboards. My background continues to be grounded in database engineering principles, including performance tuning, data modeling, and reliability across both SQL Server and PostgreSQL environments.
Technical Skills
SQL Server & Core Database
- T-SQL — advanced development, performance tuning, automation
- SQL Server Integration Services (SSIS) — ETL, data migration, workflow automation
- SQL Server Reporting Services (SSRS) — reporting, analytics, dashboards
- SQL Server Data Tools (SSDT) — Visual Studio
- VS Code
Databricks & Lakehouse Engineering
- Databricks — notebook-based data engineering, repeatable workflows, Python and Spark SQL development
- Delta Lake — ACID tables, transaction history, Time Travel concepts, versioned data processing
- Medallion Architecture — Bronze, Silver, and Gold data modeling patterns
- PySpark — JSON ingestion, nested data shaping, STRUCT and ARRAY transformations
- Spark SQL — analytical querying, point-in-time filtering, nested data expansion with explode
- SCD Type II Modeling — historical row versioning, effective dating, current and prior-state analysis
- Healthcare Data Pipelines — HL7-derived JSON, patient-condition history, diagnosis progression modeling
High Availability & Disaster Recovery
- Always On Availability Groups
- Windows Server Failover Clustering (WSFC)
- Log Shipping
Performance & Monitoring
- Query optimization, indexing strategies, execution plan analysis
- Extended Events, Resource Governor, SQL Trace, SQL Audit
Data Engineering & Automation
- ETL/ELT pipelines, workflow orchestration
- T-SQL, SSIS, PowerShell, Python
- Databricks, Power BI
Platforms & Tools
- SQL Server 7.0–2022, PostgreSQL, Azure SQL, SQL Server on Azure VMs
- GitHub, Hyper-V, Oracle VirtualBox, Docker/Dev Containers, Linux
- Linux distributions — Debian, openSUSE
Full-Stack Development
- React TypeScript, ASP.NET Core/C#
- JSON-based data models, workflow-driven applications
- MinIO object storage, HashiCorp Vault secrets management
Top Dozen Things I Own — Ranked by criticality to business continuity
- Backups & verified restores — Full, differential, and transaction log backups aligned to recovery objectives; timed and monitored for completion; followed by automated restore tests to validate integrity and prove RPO/RTO.
- HA/DR posture — FCI first for instance-level HA, then AGs for database-level HA/DR; regular failover tests and documented runbooks.
- Observability — Extended Events + Query Store baselines; DMVs and wait statistics for runtime insight; p95 latency dashboards; alerting on blocking, deadlocks, and resource pressure; proactive detection of drift from baseline. p95 dashboard.
- Performance — Query tuning and plan analysis; intelligent indexing (usage, fragmentation, missing/overlapping detection); workload characterization via XE/DMVs; regression monitoring after deployments; capturing top resource consumers and isolating bottlenecks across CPU, I/O, memory, and concurrency.
- Security — Safeguard data through least-privilege access, encrypted transport/storage, vault-managed secrets, hardened endpoints, and centralized auditing, with periodic access reviews and vulnerability checks.
- Release pipeline — Enforce database code quality with SSDT/DACPAC artifacts, schema-drift detection, pre/post deploy checks, CI/CD gates, controlled change windows, and rollback procedures.
- ETL reliability — Guarantee reliable data movement with restartable, idempotent loads; source-to-target data quality checks; late-arriving dimension handling; error quarantine/retries; and clear freshness SLAs.
- Capacity & cost — Forecast growth, right-size resources, validate backup/restore throughput, tier data to control cost, and track cloud consumption with variance dashboards and ownership tags.
- Incident response readiness — Prepare for outages with clear severity levels, on-call rotation, triage/runbooks, comms cadence, MTTA/MTTR tracking, and postmortems feeding preventive standards.
- Standards & practices — Promote consistency with a T-SQL style guide, naming/indexing conventions, approved design patterns, and knowledge sharing via checklists, guilds, and examples of “what good looks like.
- Compliance & governance — Align database controls with regulatory mandates (PII/PHI/PCI, HIPAA, SOX), ensuring encryption, immutability, access recertification, and auditable change management.
- Documentation & knowledge transfer — Reduce tribal knowledge with living runbooks, DR diagrams, environment inventory, onboarding guides, and self-serve dashboards linked from documentation.
Automation reduces toil so I can widen scope and raise quality—same workload, fewer surprises.
Technical Skillset
Core Database
- Transact-SQL (T-SQL) — advanced development, performance optimization, automation
- SQL Server Integration Services (SSIS) — ETL, data migration, process automation
- SQL Server Reporting Services (SSRS) — reporting, analytics, dashboard delivery
- SQL Server Data Tools (SSDT) in Visual Studio; daily development in VS Code
High Availability & Disaster Recovery (HADR)
- Installed, configured, and managed Always On AGs, WSFC, and Log Shipping in production
Performance & Security
- Expertise in query tuning, indexing strategies, and partitioning
- Implemented Extended Events, Resource Governor, SQL Trace, and SQL Audit for performance & compliance
Automation & Orchestration
- Designed automated solutions using T-SQL and SSIS; experience with PowerShell and Python
- Built pipelines & workflow orchestration with Airflow; analytics with Databricks; visualization in Power BI
Platforms & Utilities
- SQL Server (7.0a–2019; 2022 FCI lab)
- Windows Server 2022 WSFC lab
- Hyper-V & VirtualBox cluster labs
- PostgreSQL, Azure SQL, SQL Server on Azure VMs
- GitHub, Docker/Dev Containers, Linux openSuSE-Leap & Debian
Application & API Collaboration
- Partnered with dev teams on C# and ASP.NET Core solutions
- Hands-on with API design & testing (REST, OData, Postman, Identity Server) to improve app↔DB integration
Project Roadmap
Projects: Past, Present and Future
SQLAgent007
Open project for automated index tuning, performance surveillance, and cross‑platform support (SQL Server, Azure SQL, PostgreSQL). Repo & docs coming online.
AIMS (Modernized)
Reviving a legacy automation suite for latest SQL Server + Azure + PostgreSQL. Focus: repeatable ops, safety rails, and observability.
HL7 Clinical Data Integration
Converted and integrated HL7 data into SQL Server for pharmacy operations (OnePoint Patient Care). Reliable, auditable pipelines.
PostgreSQL Lab
Debian/Ubuntu VM lab experimenting with pgAdmin, containerization, and index/plan behavior—groundwork for cross‑platform automation.
Impact Highlights
90‑minute Outage Rescues
At Grand Canyon University, I resolved a severe performance incident in under 90 minutes that had originally been allotted two weeks for triage. I quickly isolated the bottleneck, applied corrective measures, and stabilized the system. With the crisis resolved, I used the remaining time to design and implement a data-growth forecasting utility that gave the university greater visibility into long-term capacity planning.
During a production outage at FlipSwitch, application performance collapsed after a key index was accidentally dropped. Because I had proactive monitoring in place—tracking disk queue length—I was alerted immediately before the full collapse began. I quickly diagnosed the missing index, scripted its recreation, and restored stability in under 90 minutes. The incident reinforced the value of proactive monitoring of performance metrics and resources, which enable rapid response and safeguard business continuity.
Director who still ships
Led at OnePoint Patient Care for 5+ years while staying hands-on: built and maintained HL7 integrations end-to-end, tuned high-volume OLTP workloads, and delivered reliable releases under real-world pressure. I owned the data tier while running a small, high-performing team—prioritizing a tight backlog, code reviews, and on-call readiness. When incidents surfaced, I led the response and then hardened the system (monitoring, automated tests, and runbooks) to prevent recurrence. Leadership meant clearing roadblocks and setting standards, not stepping away from the keyboard.
Automation mindset
I build tools that take noisy, fragile routines and make them quiet and predictable. My utilities handle adaptive index maintenance, statistics refresh, CHECKDB scheduling with alerts, deadlock/blocked-session capture, job-health monitoring, and restore verification—idempotent by design with clear logs and guardrails. My current project, SQLAgent007, extends this approach across SQL Server, Azure SQL, and PostgreSQL with pluggable collectors, JSON-based configs, dry-run/safe modes, and opinionated defaults for production safety.
Production pragmatist
Bias for observability, repeatability, and low-drama ops: baseline before changes, deploy with rollback plans, and prefer small, reversible steps over risky “big bang” moves. I instrument with Query Store/DMVs/Extended Events, track waits and key counters, and keep runbooks current so on-call is calm, not chaotic. I’ll say “no” to shortcuts that jeopardize uptime—think cavalier NOLOCK usage, unbounded long-running scripts, or schema changes without a cutover plan—and I pair every DR claim with routine test restores and periodic failover drills.