Skill Guide

SQL and business intelligence for real-time workforce dashboards (Looker, Metabase, Grafana)

The practice of designing, optimizing, and querying databases (SQL) to build and maintain live, interactive dashboards (using tools like Looker, Metabase, or Grafana) that visualize key workforce metrics such as headcount, attrition, and performance for real-time decision-making.

This skill enables HR and operational leaders to replace static reports with dynamic, data-driven insights, directly improving talent retention, operational efficiency, and strategic workforce planning. It transforms HR from a cost center into a strategic partner by providing evidence-based metrics for leadership decisions.

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How to Learn SQL and business intelligence for real-time workforce dashboards (Looker, Metabase, Grafana)

1. Master core SQL syntax (SELECT, FROM, WHERE, JOINs, GROUP BY, aggregate functions like COUNT, AVG). 2. Understand database schema design, specifically star or snowflake schemas for HR data (fact tables for transactions, dimension tables for employee attributes). 3. Learn basic data visualization principles: chart selection for time-series (headcount trends), categorical data (attrition by department), and key performance indicators (KPIs).

1. Practice writing complex queries with Common Table Expressions (CTEs), window functions (ROW_NUMBER, LAG) for calculating metrics like rolling attrition rates. 2. Focus on data modeling for BI: create a logical data model linking tables for employees, positions, compensation, and performance reviews. 3. Common mistake: Ignoring data freshness; learn to configure and document refresh schedules in tools like Metabase or Looker to ensure dashboard accuracy.

1. Architect scalable data pipelines from source HRIS systems (Workday, SAP) into a data warehouse (Snowflake, BigQuery) using ETL/ELT tools (dbt, Fivetran). 2. Implement row-level security (RLS) and column-level permissions in Looker or Metabase to ensure sensitive data (e.g., salaries) is only visible to authorized users. 3. Develop a KPI governance framework to define, measure, and audit workforce metrics consistently across the organization.

Practice Projects

Beginner

Project

Build a Static Headcount Dashboard

Scenario

Your HR team needs a simple dashboard showing current headcount by department and location from a CSV export of employee data.

How to Execute

1. Load the CSV into a local database (e.g., SQLite or PostgreSQL). 2. Write SQL queries to aggregate headcount by `department` and `location`. 3. Connect Metabase or Grafana to the database and create a bar chart and a summary table. 4. Share the dashboard link with your HR partner for feedback.

Intermediate

Project

Create a Real-Time Attrition Tracker

Scenario

HR Leadership wants a live dashboard to monitor monthly attrition rates by tenure band and manager, pulling data from a live HRIS database.

How to Execute

1. Write a SQL query using window functions to calculate rolling 12-month attrition rates: `(Terminations in Period / Average Headcount)`. 2. Model the data in Looker's LookML to create reusable dimensions (e.g., `tenure_band`) and measures (e.g., `attrition_rate`). 3. Build a Looker dashboard with filters for `department` and `hire_date` range. 4. Set up an automated alert in Grafana when attrition exceeds a threshold (e.g., >15%).

Advanced

Project

Design an Integrated Workforce Planning Platform

Scenario

As a People Analytics Lead, you are tasked with creating a single source of truth for all workforce data (HRIS, ATS, Performance) to power strategic planning and board reporting.

How to Execute

1. Design and build a data warehouse schema in dbt that integrates data from Workday (HRIS), Greenhouse (ATS), and Lattice (Performance). 2. Implement incremental models in dbt to efficiently update large tables (e.g., `fct_employee_snapshots`). 3. Build a Looker LookML project with defined explores, persistent derived tables, and aggregated awareness for fast dashboard performance. 4. Develop row-level security policies so Finance sees cost data, HR sees PII, and Executives see summary KPIs.

Tools & Frameworks

BI & Visualization Platforms

Looker (LookML, Explores)Metabase (Simple & Native Queries, Alerts)Grafana (Dashboard Provisioning, Alerting)

Looker excels in governed, modeled analytics with LookML; Metabase is user-friendly for ad-hoc querying by business users; Grafana is ideal for operational monitoring and real-time alerting from SQL data sources.

Data Warehousing & Transformation

Snowflake, BigQuery, PostgreSQLdbt (data build tool)Fivetran, Airbyte

Cloud data warehouses are the foundation for scalable SQL. dbt is used for version-controlled SQL transformation and testing. Fivetran/Airbyte automate data pipeline ingestion from HRIS/ATS sources.

Data Modeling Frameworks

Kimball Dimensional Modeling (Star Schema)Slowly Changing Dimensions (SCD Type 2)Metrics Layer / Semantic Layer

Kimball modeling structures data for efficient BI queries. SCD Type 2 tracks historical changes in employee attributes (e.g., role, manager). A metrics layer (like Looker's) defines business logic once for consistent KPI calculation.

Interview Questions

Answer Strategy

The candidate must demonstrate an understanding of data integration, ELT pipelines, and BI modeling. Strategy: Outline the end-to-end architecture from data ingestion to visualization, emphasizing the transformation layer. Sample Answer: "First, I'd use an ELT tool like Fivetran to replicate data from Workday (employee records) and Greenhouse (application stages) into our data warehouse (Snowflake). Then, I'd use dbt to model the data, creating a `fct_hiring_process` fact table that joins on `job_req_id` to calculate time from application submission to hire date. Finally, I'd build a Looker LookML model on this curated layer, defining the 'Time-to-Hire' measure and exposing it in a department-filtered Explore for the HR team."

Answer Strategy

This tests stakeholder management and problem-solving beyond pure technical skill. The core competency is diagnosing the root cause (data, design, or process issue) and taking corrective action. Sample Answer: "In a previous role, a quarterly attrition dashboard for managers had low usage. After interviews, I found two issues: the data refreshed too late in the month, and the metrics were too complex. I solved this by working with IT to change the refresh schedule to run nightly, and I simplified the view to show a primary 'Team Attrition Rate' with a drill-down option for details. I then hosted a 15-minute training session. Adoption tripled the next quarter."