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, semi-structured, 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,…