• Design and build a scalable, org-wide data architecture that serves as the backbone for all current and future products. • Design, build, and maintain data pipelines and ETL/ELT processes across Azure SQL Server, PostgreSQL, Elasticsearch, and third-party sources (Salesforce, APIs). • Evaluate and support a potential migration from MSSQL to PostgreSQL, including feasibility analysis, schema translation, deadlock remediation, performance benchmarking, and migration planning. • Build and maintain a centralized data warehouse or data platform to support analytics, reporting, AI/ML workflows, and our broader data-as-a-service. • Architect metadata models, taxonomies, and tagging systems that enable content enrichment across products (e.g., tagging content with vendors, team size, revenue, industry). • Collaborate with engineering, product, and AI teams to define data models and ensure clean, consistent data flows across systems. • Implement data quality checks, monitoring, and alerting to maintain data integrity across all data products. • Document data architecture, lineage, and dictionary standards for the engineering team. • Support and improve existing Azure SQL databases, Redis caching layers, and Elasticsearch clusters. • Identify and resolve data bottlenecks, query performance issues, and infrastructure gaps.

• Design and maintain scalable data pipelines. • Structure, transform, and optimize data in Snowflake. • Implement multi-source ETL/ELT flows (ERP, APIs, files). • Leverage the AWS environment, including S3, IAM, and various data services. • Prepare data for Data Science teams and integrate AI/ML models into production. • Ensure data quality, security, and governance. • Provide input on data architecture.

• The Nova team is building a “single pane of glass” for investigators. The purpose is to connect the investigator to the many disparate sources of data they already have access to in order to make it easier to “connect the dots” and build a picture around their case work. • The technical challenges to enable these tools are diverse and sophisticated. • The team needs a strong engineer to build integrations to the many data sources as well as build stronger bindings to the overall Flock Software Platform. • Our engineers work directly with our customers and are able to react with high value and often real life impacting results. • Flock believes that many facets of our work are “Better Together” and that infuses how we think about the various products we offer as well as how we work together individually and collectively. This provides a vast opportunity to work across multiple teams and skillsets and not only elevate the company but our engineers as well.

• Define the enterprise data architecture vision aligned with business strategy and digital transformation goals. • Design conceptual, logical, and physical data models for domain-driven systems. • Establish target-state architectures for data platforms (cloud, hybrid, on-prem). • Evaluate and select core technologies, taking into consideration specific customer needs, priorities and current architecture • Architect scalable data pipelines (ETL/ELT). • Define integration standards (APIs, CDC, event-driven architectures). • Optimize data lake, lakehouse, and warehouse architectures. • Design high availability, disaster recovery, and backup strategies. • Enforce security controls: encryption, IAM, row-level security, masking. • Evaluate emerging paradigms (data mesh, lakehouse, streaming-first architectures). • Assess tools for orchestration and transformation (e.g., Apache Airflow, dbt). • Prototype proof-of-concept architectures. • Ensure architectural flexibility for AI, advanced analytics, and real-time insights.