What a Lead } should know

Defines code quality standards for BI team: DAX/SQL naming conventions, dashboard review processes, report template governance. Conducts architectural reviews of data model designs. Establishes quality gates for metric definitions and report publishing workflows.

Defines data structure standards for BI team: dimensional modeling conventions, data model review processes, metric definition governance. Conducts reviews of analytical data models for performance and clarity. Establishes team guidelines for report data structure optimization.

Defines Elasticsearch/OpenSearch architecture for BI organization: index lifecycle management, Kibana space governance, and dashboard performance standards. Establishes search query optimization guidelines and data retention policies.

Defines architecture for BI web applications and embedded analytics platforms using Flask and Dash. Establishes standards for report API design and caching strategies. Conducts design reviews of dashboard backends and defines the roadmap for self-service analytics tooling.

Defines ClickHouse architecture standards for the BI platform, including cluster topology, replication policies, and data retention strategies. Establishes governance for materialized view lifecycle and schema evolution. Mentors analysts on query optimization patterns and coordinates ClickHouse upgrades with minimal dashboard downtime.

Defines data modeling standards for BI organization. Establishes naming conventions, modeling guidelines, and review processes. Coordinates dimensional model governance across teams with conformance rules and shared dimension management.

Defines indexing strategy for the BI platform: standardized index management policies, performance benchmarks, and capacity planning for analytical workloads. Conducts reviews of database schema changes and their indexing impact. Establishes monitoring and alerting for index performance degradation across BI data stores.

Establishes organization-wide standards for MySQL analytical layer design including naming conventions, view hierarchies, and aggregation table refresh strategies. Coordinates cross-team BI connectivity architecture ensuring consistent Tableau and Power BI data source configurations. Evaluates MySQL vs MariaDB feature trade-offs for enterprise BI workloads and drives technology selection decisions.

Defines PostgreSQL data strategy for BI teams: establishes standards for analytical schema design and query optimization, designs data warehouse architecture for enterprise reporting, drives adoption of PostgreSQL best practices for analytical workloads.

Defines query optimization strategy for BI department. Establishes SQL performance standards, query review processes, and performance monitoring requirements. Conducts reviews of critical analytical queries. Creates query optimization guidelines and training programs for BI analysts.

Defines API documentation strategy for enterprise BI and data platform integrations. Establishes design standards for data access API documentation including data governance annotations and usage guidelines. Coordinates cross-team documentation reviews ensuring data API consistency across analytics platforms.

Defines API strategy for BI data access at product level: data API layer standards for reporting, API-based data integration governance, analytics API performance requirements. Conducts API architecture reviews for BI platform integrations.

Defines AWS infrastructure strategy for enterprise BI platforms spanning data lakes, warehouses, and analytics services. Establishes IaC standards for Redshift/Glue/QuickSight deployments and data governance. Conducts architecture reviews optimizing data workload costs and drives FinOps for analytics compute spending.

Defines Docker infrastructure strategy for BI platforms: containerized analytics stack governance, data persistence and backup standards, container performance tuning frameworks. Conducts architecture reviews for containerized BI deployments and optimizes FinOps for analytics workloads.

Defines BI data pipeline strategy and Airflow platform standards. Establishes DAG development guidelines, code review practices, and deployment workflows for reporting team. Coordinates data freshness SLAs with stakeholders.

Defines enterprise BI strategy and dashboard platform roadmap. Shapes self-service BI culture enabling business-driven analytics. Coordinates BI teams across departments and standardizes KPI definitions. Optimizes hybrid approaches combining Tableau, Power BI, and Looker ecosystems.

Defines data catalog governance strategy for BI organization. Establishes metadata quality standards, stewardship roles, and catalog adoption metrics. Drives cultural shift toward data-as-a-product through catalog-first workflows.

Defines data contract governance strategy for BI organization. Establishes contract lifecycle management processes, ownership models, and cross-team negotiation protocols. Measures contract adoption and violation reduction metrics.

Defines data lineage governance strategy for BI organization. Establishes lineage completeness standards, ownership models, and integration requirements across data stack. Drives lineage-powered compliance and audit capabilities.

Defines data quality strategy across BI and analytics teams. Coordinates data mesh principles with embedded quality gates. Shapes platform roadmap prioritizing observability, lineage, and quality automation. Drives cross-team alignment on data contracts and SLAs.

Defines the organization-wide warehouse schema strategy for BI consumption, aligning star and snowflake designs with long-term reporting roadmaps. Establishes standards for aggregate table creation, materialized view governance, and schema versioning. Coordinates with data engineering leadership to ensure warehouse evolution supports both operational and strategic BI initiatives.

Defines dbt development standards for BI organization. Establishes model naming conventions, testing requirements, and code review processes. Coordinates dbt governance across teams with shared macro libraries and package management strategies.

Defines data engineering strategy for BI organization. Shapes data platform architecture: tool selection (Pandas vs Polars vs Spark), data pipeline standards, and data quality governance. Coordinates data teams on shared transformation libraries and best practices. Optimizes data mesh/data fabric approaches for self-service analytics.

Defines BI warehouse ETL strategy and standards across teams. Governs dimensional modeling conventions, orchestrates cross-domain data integration, and establishes SLA-driven refresh schedules for executive dashboards.

Defines ChatGPT/Claude adoption strategy for BI and analytics teams. Establishes standards for AI-assisted data analysis, automated reporting, and prompt libraries for analytical workflows. Conducts reviews of AI integration patterns in BI development ensuring data accuracy and responsible insight generation.

Defines GitHub Copilot strategy for BI teams: establishes guidelines for AI-assisted SQL and ETL development, designs validation workflows for AI-generated data transformation code, drives adoption of Copilot best practices across analytics development processes.

Defines prompt engineering strategy for the BI department. Establishes standards for AI-assisted analytics: prompt templates, output validation, and quality metrics. Creates governance framework for AI usage in business reporting — accuracy requirements, human review gates, and audit trails.

Defines product observability strategy. Establishes SLO-based approach. Coordinates incident management. Optimizes MTTD/MTTR.

Defines observability strategy for BI data platforms: establishes SLO-based approach for data pipeline reliability and freshness, coordinates data incident management, optimizes MTTD/MTTR for data quality incidents through improved monitoring.

Defines observability strategy for BI data platforms: establishes SLO-based approach for data pipeline reliability and freshness, coordinates data incident management across analytics teams, optimizes MTTD/MTTR for data quality incidents.

Defines code review strategy for BI team: SQL/DAX review standards, dashboard code review governance, analytical methodology review practices. Establishes review culture for analytical code quality and metric consistency.

Defines Git strategy for BI teams: establishes branching standards for multi-environment report development, designs review workflows for analytical code changes, drives adoption of version control best practices for SQL and ETL pipelines across analytics teams.