Build HR Analytics Dashboards

This skill is expert in designing, developing, and implementing HR analytics dashboards, translating workforce data into actionable insights through visualizations, KPI frameworks, and interactive reporting. Its essential metrics framework spans talent acquisition (time-to-hire, cost-per-hire, source effectiveness, candidate quality), retention and turnover (voluntary/involuntary rates, retention by segment, exit analysis), performance management (distribution, goal completion, review cycle metrics), employee engagement (eNPS, survey participation, engagement drivers), diversity and inclusion (representation, pay equity, promotion rates by demographics), workforce planning (headcount trends, span of control, succession pipeline), and learning and development (training completion, skill gaps, training ROI). It structures dashboards in three layers - strategic (C-suite, quarterly trends, industry benchmarks), tactical (HR leadership, monthly deep-dives, departmental comparisons), and operational (HR teams, real-time daily/weekly monitoring) - and covers advanced analytics like a SQL-based flight-risk scoring model combining tenure, performance rating, and time-since-promotion into a risk category, and a diversity metrics function computing gender balance, ethnic diversity via a Simpson diversity index, and pay equity analysis across job level, department, and tenure.

Use this skill when building an HR analytics dashboard that needs real predictive and diversity analytics, not just headcount charts - it names concrete visualization patterns (heat maps for department performance, funnel charts for recruitment pipelines, cohort analysis for hiring-class retention) and a real-time-versus-batch processing split (real-time for active headcount and new hires, hourly for survey responses, daily batch for trend analysis, weekly/monthly for full benchmarking). Its security and compliance framework is central, not optional: role-based dashboard access, row-level security for sensitive employee data, audit logging, anonymization for aggregate reporting, and GDPR compliance, including a privacy-preserving turnover function that suppresses groups below a minimum size threshold before reporting rates. It is not meant for ad hoc HR reporting without access controls - the privacy-first analytics approach is a load-bearing part of the design, not an add-on.

-- Flight Risk Analysis Query
WITH employee_features AS (
  SELECT 
    employee_id,
    tenure_months,
    last_performance_rating,
    salary_percentile_in_grade,
    manager_span_of_control,
    career_moves_count,
    days_since_last_promotion,
    engagement_score_latest
  FROM hr_analytics_view
  WHERE active_status = 'Active'
),
risk_scoring AS (
  SELECT *,
    CASE 
      WHEN tenure_months BETWEEN 6 AND 24 THEN 3
      WHEN tenure_months BETWEEN 24 AND 60 THEN 1
      ELSE 2
    END +
    CASE
      WHEN last_performance_rating < 3 THEN 4
      WHEN last_performance_rating >= 4 THEN 0
      ELSE 2
    END AS flight_risk_score
  FROM employee_features
)
SELECT employee_id, flight_risk_score
FROM risk_scoring
ORDER BY flight_risk_score DESC;

Given workforce data, the skill produces executive metrics classes (workforce overview, talent pipeline health), the flight-risk scoring query above with High/Medium/Low risk categorization, diversity metrics calculations across job levels, a dynamic cascading filter system for department/job-level/location/date-range, and privacy-preserving aggregate turnover analysis that suppresses small groups before reporting. It also recommends a technology stack: Tableau/Power BI/React+D3.js for visualization, Airflow/dbt for pipelines, Snowflake/BigQuery for analytics storage, and FastAPI/Django REST for a custom metrics API.

HR analytics and People Ops teams building dashboards that need predictive flight-risk scoring, real diversity and pay-equity analysis, and privacy-compliant reporting rather than static headcount charts. It suits organizations that need role-based access and audit logging built into the dashboard from the start, and that track dashboard adoption itself (user engagement, time-to-insight, reduction in manual reporting requests) as a success metric.