Senior Data Architect

Apply today to join Coreforce, where your Data Architect expertise makes a real impact.

Join Our Team as a Senior Data Architect

Company: Coreforce
Location: Atlanta
Job Type: Full-time
Salary: Based on Experience

Company Overview:
Coreforce is an innovative SaaS company providing digital solutions for frontline professionals. Our products, body cameras, in-car videos, mobile routers, and digital evidence systems help public safety officers and first responders save lives, strengthen community trust, and enhance accountability.

Senior Data Architect – Build Your Career with Purpose

Join Coreforce and use your data architect skills to support innovative technology that strengthens communities.

Why You’ll Love Working Here:

• Flexible hybrid schedule

• Free chef-inspired lunch Mon–Thu

• Competitive benefits: medical, dental, vision, 401(k). We provide 401(k) matching per the terms of the 401(k) plan.

• 15 PTO days + floating holiday

• Annual bonus and tuition reimbursement

• Career growth in a fast-growing, mission-driven company

• Collaborative, purpose-driven culture

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.