MTech Systems · 12 hours ago
Sr Data Architect
MTech Systems is focused on building technology that solves real problems for the world, and they are seeking a Sr Data Architect to own the database/data platform architecture for their customer-facing SaaS and analytics products. This role involves driving performance, reliability, and auditability at scale while leading the DBA/DB engineering function and collaborating with various teams to meet customer reporting needs.
Responsibilities
Own the end-to-end data architecture for enterprise SaaS (OLTP + analytical serving), including Azure SQL/MI, Databricks/Delta Lake, ADLS, Synapse/Fabric, and collaboration on Power BI semantic models (RLS, performance)
Define and implement Information Lifecycle Management (ILM): hot/warm/cold tiers, 2-year OLTP retention, archive/nearline, and a BI mirror that enables rich analytics without impacting production workloads
Engineer ERP/SAP financial interfaces for idempotency, reconciliation, and traceability; design rollback/de-dup strategies and financial journal integrity controls
Govern schema evolution/DbVersions to prevent cross-customer regressions while achieving performance gains
Establish data SLOs (freshness, latency, correctness) mapped to customer SLAs; instrument monitoring/alerting and drive continuous improvement
Build observability for pipelines and interfaces (logs/metrics/traces, lineage, data quality gates) and correlate application telemetry (e.g., Stackify/Retrace) with DB performance for rapid root cause analysis
Create incident playbooks (reprocess, reconcile, rollback) and drive MTTR down across data incidents
Lead the DBA/DB engineering function (standards, reviews, capacity planning, HA/DR, on-call, performance/availability SLOs) and mentor data engineers
Partner with Product/Projects/BI to shape domain models that meet demanding customer reporting (e.g., Tyson Matrix) and planning needs without compromising OLTP
Qualification
Required
15+ years in data/database engineering; 5–8+ years owning data/DB architecture for customer facing SaaS/analytics at enterprise scale
Proven results at multi-terabyte scale (≥5 TB) with measurable improvements (P1 reduction, MTTR, query latency, cost/performance)
Expertise in Azure SQL/MI, Databricks/Delta Lake, ADLS, Synapse/Fabric; deep SQL, partitioning/indexing, query plans, CDC, caching, schema versioning
Audit & SLA readiness: implemented controls/evidence to satisfy SOC 1 Type 2 (or equivalent) and run environments to SLOs linked to SLAs
ERP/SAP data interface craftsmanship: idempotent, reconciled, observable financial integrations
ILM/Archival + BI mirror design for queryable archives/analytics without OLTP impact
Power BI performance modeling (RLS, composite models, incremental refresh, DAX optimization)
Modular monolith/microservices experience
Preferred
Semantic tech (ontology/knowledge graphs), vector stores, and agentic AI orchestration experience (advantage, not required)