What a Principal } should know

Defines organizational code quality strategy for BI and analytics: enterprise metric governance, cross-team dashboard standardization, data model quality frameworks. Makes decisions on BI tooling and establishes organization-wide reporting quality standards.

Defines organizational data structure strategy for BI: enterprise semantic layer design, cross-team metric governance, data model standardization frameworks. Makes strategic decisions on analytical data platform architecture and tooling investments.

Defines enterprise Elasticsearch/OpenSearch strategy for BI and observability. Evaluates search platform evolution and migration paths. Shapes organizational standards for search-powered analytics and real-time data exploration.

Defines company-wide strategy for BI web platforms, evaluating Dash, Streamlit, and Django for embedded analytics. Establishes enterprise standards for report API architectures, data access layers, and reference patterns for self-service analytics applications.

Drives organization-wide ClickHouse adoption strategy for BI analytics, evaluating trade-offs against alternatives like Druid or BigQuery. Designs multi-tenant ClickHouse architectures with resource isolation and cost attribution. Shapes the technical roadmap for real-time BI capabilities, integrating ClickHouse with streaming platforms and semantic layers.

Defines enterprise data modeling strategy spanning operational and analytical systems. Evaluates modeling methodologies (Kimball, Inmon, Data Vault) and shapes organizational standards. Drives data model governance framework aligned with data mesh principles.

Defines organizational data strategy for business intelligence: database technology selection for analytical workloads, multi-region data architecture, and real-time analytics infrastructure. Evaluates emerging technologies (vector databases, time-series databases) for BI use cases. Drives adoption of performance-centric data architecture across the organization.

Defines enterprise data platform strategy positioning MySQL within the broader analytical ecosystem alongside data warehouses and lakehouse solutions. Architects multi-region MySQL deployments for global BI reporting with sub-second query latency requirements. Drives innovation in real-time BI by leveraging MySQL change data capture and streaming integration with modern BI platforms.

Defines organizational data strategy for enterprise analytics: evaluates PostgreSQL vs specialized analytical databases for organizational BI, designs multi-region data warehouse architectures, establishes governance for analytical data modeling and query optimization standards.

Defines organizational data strategy for business analytics: query engine selection for different analytical workloads, data access architecture standardization, and performance governance at enterprise scale. Evaluates emerging query technologies (vectorized engines, GPU-accelerated queries). Drives data engineering excellence across the organization.

Defines organizational API documentation strategy for enterprise data and analytics platforms. Designs platform-level documentation architecture for data access APIs with integrated governance metadata and usage analytics. Establishes enterprise API documentation standards balancing self-service accessibility with data governance compliance.

Defines organizational API strategy for analytics: enterprise data API platform standards, cross-team analytics API governance, API infrastructure investment decisions for BI workloads. Designs enterprise-grade API architecture for analytics platforms.

Defines organizational cloud strategy for BI and analytics workloads evaluating multi-cloud data platforms vs AWS-native services. Designs enterprise-grade data infrastructure with unified data lakes, governed access patterns, and cross-account analytics services. Establishes FinOps practices for data storage and compute optimization across analytics teams.

Defines organizational container strategy for analytics: enterprise BI platform containerization standards, cross-team data service governance, analytics compute resource frameworks. Designs enterprise-grade container infrastructure for analytics workloads and establishes FinOps practices.

Defines enterprise data orchestration strategy spanning Airflow, dbt, and BI tools. Evaluates orchestration platforms and migration paths. Shapes organizational data delivery standards and cross-team pipeline governance.

Defines organizational BI strategy aligning dashboard platforms with business objectives. Designs enterprise-wide self-service BI architecture spanning Tableau, Power BI, and Looker. Establishes cross-departmental KPI governance framework ensuring data-driven decision-making at executive level.

Defines enterprise data catalog strategy spanning all data domains. Evaluates catalog platforms (DataHub, Atlan, Alation) and shapes metadata architecture. Establishes organizational data governance framework with catalog as central pillar.

Defines enterprise data contract strategy spanning all organizational data domains. Shapes contract specification standards and tooling decisions. Drives industry-aligned practices for data mesh contract governance.

Defines enterprise data lineage strategy spanning all data domains and tooling layers. Evaluates lineage platforms (OpenLineage, Marquez, DataHub) and shapes metadata architecture. Drives regulatory compliance through lineage-based audit trails.

Defines enterprise data quality vision across BI, analytics, and reporting. Architects governance frameworks with automated quality enforcement for Tableau, Power BI, and cloud warehouses. Establishes standards for data certification, lineage, and trust scoring. Influences industry practices.

Shapes the enterprise data warehouse vision for BI, driving adoption of modern patterns like data mesh semantic layers and unified metrics platforms. Evaluates emerging warehouse technologies and their impact on BI schema design at organizational scale. Influences vendor and platform decisions, ensuring warehouse architecture enables self-service analytics while maintaining data quality and governance standards.

Defines enterprise dbt strategy and transformation platform architecture. Evaluates dbt Cloud vs Core trade-offs, Mesh adoption, and semantic layer integration paths. Shapes organizational data transformation governance aligned with data mesh principles.

Defines organizational data strategy for business intelligence: enterprise data platform design, data governance framework, and self-service analytics vision. Evaluates emerging data technologies for BI transformation. Drives data-driven culture adoption across the organization. Shapes data literacy and tooling standards at enterprise level.

Shapes organization-wide BI data platform vision and ETL architecture. Drives adoption of modern ELT patterns, defines enterprise semantic layer standards, and aligns warehouse ETL strategy with long-term business intelligence roadmap.

Defines organizational strategy for ChatGPT/Claude adoption across BI and analytics functions. Establishes enterprise-wide AI-assisted analytics frameworks balancing automation with data governance and analytical rigor. Mentors leads and architects on strategic AI integration for data-driven decision-making at organizational scale.

Defines organizational GitHub Copilot strategy for analytics: evaluates enterprise approaches for AI-assisted data engineering, designs governance for AI-generated analytical code quality, establishes standards for responsible AI tool adoption across analytics organizations.

Defines enterprise AI-augmented analytics strategy. Establishes organizational standards for AI-assisted decision-making in business intelligence — accuracy benchmarks, governance policies, and ethical guidelines. Drives cross-departmental AI adoption for data-driven insights. Shapes vendor relationships and model procurement strategies for BI use cases.

Defines organizational observability strategy. Implements platform solutions. Builds reliability culture. Establishes enterprise SLO framework.

Defines organizational observability strategy for data analytics with Prometheus & Grafana: implements enterprise data pipeline monitoring, builds data reliability culture across analytics teams, establishes enterprise SLO framework for data quality and freshness at scale.

Defines organizational observability strategy for data analytics: implements enterprise data pipeline monitoring platforms, builds data reliability culture across analytics teams, establishes enterprise SLO framework for data quality and pipeline freshness at organizational scale.

Defines organizational code review strategy for analytics: enterprise analytical code review standards, cross-team review governance for BI codebases, review culture maturity model for analytics teams. Mentors leads on analytical code review best practices.

Defines organizational Git strategy for analytics: evaluates version control approaches for enterprise BI assets, designs governance for analytical code and report versioning, establishes enterprise standards for data transformation pipeline version management across analytics teams.