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Position Summary

The Senior Data Architect is responsible for defining and evolving scalable, canonical data models and data architecture patterns across Coreforce platforms. This role requires deep experience with relational, document, cache, warehouse, and other structured, semi-structured, and unstructured data stores. The Senior Data Architect will partner with engineering, product, and architecture teams to ensure data models, storage strategies, reporting foundations, and AI-ready data capabilities are reliable, performant, secure, and designed for long-term scale.

Essential Duties and Responsibilities

Data Architecture and Canonical Modeling

·       Define scalable, canonical data models that support product capabilities, integrations, analytics, reporting, and AI-enabled use cases.

·       Establish enterprise data modeling standards, naming conventions, domain models, schema design practices, and data lifecycle patterns.

·       Translate business and product requirements into durable logical and physical data models across operational and analytical systems.

·       Guide engineering teams in designing consistent data contracts, entity relationships, event structures, streaming data models, metadata models, and integration patterns.

Database and Data Store Strategy

·       Architect solutions using MySQL, PostgreSQL, MongoDB, and other structured, semistructured, and unstructured data stores.

·       Design and govern caching strategies using Redis or similar caching technologies to improve application performance and scalability.

·       Evaluate and recommend appropriate database, storage, indexing, partitioning, replication, and archival strategies based on workload characteristics

·       Support hybrid data architectures spanning transactional databases, document stores, object storage, search systems, data warehouses, and reporting platforms.

Streaming Data and Event-Driven Architecture

·       Design and govern streaming data architectures that support real-time ingestion, event processing, analytics, operational workflows, and downstream integrations.

·       Define standards for event schemas, message contracts, topic design, partitioning, ordering, retention, replay, dead-letter handling, and consumer resiliency.

·       Partner with engineering teams to evaluate and implement streaming platforms and patterns such as Kafka, Amazon Kinesis, or comparable event streaming technologies.

·       Ensure streaming data pipelines meet requirements for scalability, reliability, observability, security, compliance, latency, and data quality.

Performance, Optimization, and Capacity Planning

·       Lead database optimization efforts including query tuning, indexing strategy, schema refinement, storage layout, and performance troubleshooting.

·       Perform capacity planning for data platforms, accounting for growth, retention, throughput, latency, concurrency, and cost.

·       Define standards for observability, monitoring, alerting, backup, recovery, high availability, and disaster recovery for critical data stores.

·       Partner with engineering and operations teams to improve reliability, scalability, and cost efficiency of production data systems.

Data Warehousing, BI, and Reporting

·       Design and support data warehousing architectures that enable reliable analytics, operational reporting, compliance reporting, and executive dashboards.

·       Develop dimensional, normalized, and hybrid models appropriate for BI reporting solutions and analytical workloads.

·       Work with stakeholders to ensure data pipelines, marts, semantic layers, and reporting datasets are accurate, governed, and understandable.

·       Establish patterns for data quality, lineage, governance, cataloging, retention, and access control across reporting and analytical platforms.

AI-First Data Enablement

·       Apply an AI-first mindset to data architecture by designing data structures, metadata, retrieval patterns, and governance models that support machine learning, generative AI, search, and automation use cases.

·       Identify opportunities to use AI-assisted tooling to improve data modeling, documentation, quality analysis, anomaly detection, reporting, and operational efficiency.

·       Ensure data architecture decisions support secure, explainable, and auditable AI-enabled workflows.

Cross-Functional Leadership

·       Collaborate with principal architects, software architects, engineering leads, product managers, and operations stakeholders.

·       Review data-related designs, migrations, pull requests, and implementation plans for architectural alignment and operational readiness.

·       Mentor engineers and database practitioners on data modeling, database optimization, caching, warehousing, and reporting best practices.

·       Create clear architecture documentation, standards, diagrams, migration plans, and decision records.

Required Qualifications

Knowledge, Skills, and Abilities

·       Strong hands-on experience with MySQL and PostgreSQL in production environments.

·       Strong experience with MongoDB and document-oriented data modeling.

·       Experience designing solutions across structured, semi-structured, and unstructured data stores.

·       Required experience with caching solutions such as Redis, including cache design, invalidation, consistency, and performance tradeoffs.

·       Required expertise in data modeling, canonical model definition, schema design, and database normalization/denormalization strategies.

·       Required experience with database optimization, query tuning, indexing, partitioning, replication, and performance troubleshooting.

·       Required experience designing and operating data streaming solutions, including event-driven architectures, stream processing patterns, event schema design, and real-time data pipeline reliability.

·       Required experience with capacity planning for high-volume, production data systems.

·       Required experience with data warehousing concepts, architectures, dimensional modeling, and analytical data design.

·       Required experience with BI reporting solutions, semantic layers, dashboards, and reporting datasets.

·       Ability to define scalable data models that support operational systems, analytics, integrations, and AI-enabled capabilities.

·       AI-first mindset with a practical understanding of how data architecture enables AI, machine learning, retrieval, search, automation, and advanced analytics.

·       Strong communication skills with the ability to explain complex data architecture decisions to technical and non-technical audiences.

Preferred Qualifications

·       Experience with cloud-native data services in AWS, Azure, or GovCloud environments.

·       Experience with object storage, data lakes, search platforms, streaming/event-driven architectures, or large-scale media metadata systems.

·       Experience with data governance, data cataloging, lineage, privacy, security, compliance, and retention requirements.

·       Experience modernizing legacy data platforms or leading large-scale database migrations.

Experience supporting public safety, law enforcement, corrections, digital evidence, video, or mission-critical SaaS platforms.