Executive Summary

This analysis examines Pittsburgh’s Data Analyst and Business Intelligence (DA/BI) job market to inform strategic career transition timing for professionals pursuing analytics credentials to supplement extensive operational experience. Using Federal Reserve employment data scaled with multi-platform job posting snapshots, the study projects market conditions through Q1 2027 and assesses competitive saturation.

Project Methodology: AI‑Augmented Analytics

This project was developed using a human‑led analytical workflow supported by AI tools for structure, verification, and documentation. All core analytical work—including data collection, modeling, coding, visualization, and interpretation—was performed by the analyst.

Manual Data Work (Analyst)

Collected and validated all raw data, including the Pittsburgh_DA_BI_PlatformBreakdown_Dec2025 dataset and the indeed_pittsburgh_index series. Performed all data cleaning, modeling, and visualization development.

AI‑Supported Workflow

  • Claude: Assisted with analytical framing, scenario structuring, and narrative refinement.
  • Microsoft Copilot: Supported code troubleshooting, calculation checks, and documentation clarity.

AI tools served as strategic advisors and quality‑assurance partners. All assumptions, analytical decisions, and final outputs were determined by the analyst.

Development Time: ~10 days (vs. typical 2–3 weeks)
Technical Environment: FRED economic data (manual extraction) | Multi‑platform job posting aggregation | Excel/Google Sheets | R | Google Workspace

Key Findings

  • Market Recovery: DA/BI postings recovered 62.8% from pandemic low (Q2 2020: 94 jobs) to current levels (Q4 2024: 154 jobs)
  • Q1 2027 Projections: Expected range of 176-271 job postings depending on economic scenario
  • Market Saturation: Supply-demand ratio of 1.86:1 (moderate scenario) indicates competitive but healthy market conditions
  • Recommendation: Q1 2027 (February job search launch) represents favorable entry timing across all projection scenarios for experienced professionals with comprehensive analytics credentials

ASK Phase: Defining the Business Question

Business Question

Given Pittsburgh’s AI and technology investment trajectory (2015-2025), what is the employment outlook for mid-level Data Analyst and Business Intelligence roles in 2026-2027?

Stakeholders

Primary Stakeholder: Career transitioner with 15+ years professional experience in financial services or operations evaluating market entry timing for mid-level Business Intelligence analyst roles in Pittsburgh.

Key Decision: When to launch job search (Q4 2026 vs. Q1 2027 vs. Q2 2027) to maximize employment opportunities while completing comprehensive analytics certification pathway.

Success Metrics

  • Employment growth rates (year-over-year and quarter-over-quarter)
  • Job posting velocity trends (2020-2025)
  • Projected job availability (Q1 2027)
  • Market saturation assessment (supply vs. demand)

Strategic Context

This analysis supports a 15-month career transition roadmap targeting Pittsburgh’s healthcare, financial services, and technology sectors. Understanding market timing is critical for:

  • Completing comprehensive certification pathway (Google Data Analytics, IBM Business Intelligence, Google Advanced Data Analytics)
  • Identifying favorable entry windows for mid-level roles
  • Assessing competitive landscape among experienced professionals
  • Setting realistic salary expectations aligned with mid-career positioning

Mid-Level Role Qualification:

This analysis targets mid-level Business Intelligence Analyst positions (equivalent to 3-7 years analytics experience). Qualification pathway combines:

  • 15+ years professional experience in operations, finance, or related domains
  • Comprehensive analytics certification sequence (multiple professional certificates, not single credential)
  • Portfolio of applied projects demonstrating technical proficiency

This combination positions career transitioners competitively against candidates with analytics-only backgrounds who lack operational domain expertise.

PREPARE Phase: Data Sources and Collection

Primary Data Source: FRED Job Postings Index

Source: Federal Reserve Economic Data (FRED)
Series ID: IHLIDX38300 (Indeed Job Postings Index: Pittsburgh, PA MSA)
Baseline: February 1, 2020 = 100
Coverage: Daily observations, Q1 2020 - Q4 2024
Aggregation: Quarterly averages for trend analysis

Data Quality Assessment (ROCCC):

  • Reliable: Federal Reserve data, consistent methodology
  • Original: First-party source from Indeed Hiring Lab
  • Comprehensive: 5-year coverage spanning pandemic and recovery
  • Current: Through Q4 2024, with Dec 2025 validation snapshot
  • Cited: Public domain, fully documented

Validation Data: Job Posting Snapshot

Collection Date: December 18, 2025
Platforms: LinkedIn, Indeed, Glassdoor
Methodology: Manual search with location and recency filters

Results:

Platform Data Analyst Business Intelligence Notes
LinkedIn 24 157 BI includes broader analytics roles
Indeed 70 51 Within 50 miles
Glassdoor 83 77 Pittsburgh location
Average 59 95 Total: 154 postings

Platform Exclusion:
ZipRecruiter (908 DA, 214 BI) excluded due to duplicate listings and irrelevant search results inconsistent with other platforms.

Supporting Context

Major Employers Identified:
Healthcare (UPMC, Highmark, University of Pittsburgh), Financial Services (PNC Bank, BNY Mellon, FNB), Consulting (Deloitte, KPMG, CGI), Technology (Google, Duolingo, Affirm), Government/Research (FBI, RAND Corporation)

Common Skills: SQL (universal), Tableau/Power BI (visualization), Python/R (programming), Excel (foundational), Alteryx/NetSuite (specialized)

PROCESS Phase: Data Cleaning and Transformation

Aggregation Methodology

Raw Data: 1,825 daily observations (Q1 2020 - Q4 2024)

Processing Steps:

  1. Load FRED daily index values
  2. Convert observation dates to quarter identifiers (lubridate)
  3. Aggregate to quarterly statistics:
    • Average index (primary measure)
    • Minimum/maximum index (volatility range)
    • Number of observations per quarter
# Load aggregated quarterly data
indeed_quarterly <- read_csv("output/pittsburgh_job_postings_quarterly.csv")

# Display sample
kable(head(indeed_quarterly, 10), 
      caption = "Sample: Quarterly Indeed Job Postings Index (Pittsburgh MSA)")
Sample: Quarterly Indeed Job Postings Index (Pittsburgh MSA)
year quarter avg_index min_index max_index num_observations period
2020 Q1 96.14213 77.50 100.89 47 Q1 2020
2020 Q2 64.96396 59.57 76.14 91 Q2 2020
2020 Q3 79.66946 73.21 84.22 92 Q3 2020
2020 Q4 87.18848 84.51 88.80 92 Q4 2020
2021 Q1 95.52511 87.04 108.68 90 Q1 2021
2021 Q2 117.31945 108.95 123.59 91 Q2 2021
2021 Q3 126.82152 123.80 130.00 92 Q3 2021
2021 Q4 135.75837 127.71 141.23 92 Q4 2021
2022 Q1 143.34100 139.01 148.80 90 Q1 2022
2022 Q2 146.77044 144.95 148.72 91 Q2 2022

Scaling Methodology

Challenge: FRED index measures overall job market activity but does not isolate DA/BI roles specifically.

Solution: Scale historical index using current DA/BI market snapshot.

Calculation:

  • Current snapshot: 154 DA/BI jobs (Dec 2025)
  • Q4 2024 index: 107
  • Scaling ratio: 154 ÷ 107 = 1.44 jobs per index point

Application: Multiply historical quarterly index values by 1.44 to estimate DA/BI posting volumes.

Assumption: DA/BI roles maintain consistent share (~1.4%) of total Pittsburgh job market over analysis period.

# Apply scaling ratio
scaling_ratio <- 1.44

dabi_estimates <- indeed_quarterly %>%
  mutate(
    dabi_jobs_estimate = round(avg_index * scaling_ratio),
    dabi_jobs_low = round(min_index * scaling_ratio),
    dabi_jobs_high = round(max_index * scaling_ratio)
  )

# Display results
kable(tail(dabi_estimates %>% 
             select(period, avg_index, dabi_jobs_estimate, dabi_jobs_low, dabi_jobs_high), 8),
      caption = "DA/BI Job Estimates (Recent Quarters)")
DA/BI Job Estimates (Recent Quarters)
period avg_index dabi_jobs_estimate dabi_jobs_low dabi_jobs_high
Q2 2023 133.3863 192 188 196
Q3 2023 132.0223 190 188 193
Q4 2023 128.7239 185 171 192
Q1 2024 117.6210 169 167 172
Q2 2024 114.4614 165 162 169
Q3 2024 112.3424 162 160 164
Q4 2024 107.2009 154 152 161
Q1 2025 106.5840 153 152 155

Data Quality Limitations

Identified Issues:

  1. BLS Data Lag: Most recent complete BLS employment data through 2023; 2024-2025 estimated using trailing growth rates
  2. Platform Bias: LinkedIn’s broader BI categorization (157 postings) likely includes adjacent roles (Business Systems Analyst, Analytics Engineer)
  3. Temporal Volatility: Job posting counts fluctuate daily; snapshot represents point-in-time measurement

Mitigation Strategies:

  • Three-platform averaging reduces individual platform bias
  • Confidence intervals calculated using quarterly min/max volatility
  • Transparent documentation of assumptions and limitations

SHARE Phase: Key Insights and Visualizations

Summary of Findings

1. Market Has Fully Recovered from Pandemic

Pittsburgh’s DA/BI market rebounded 62.8% from Q2 2020 pandemic low and now exceeds pre-COVID baseline by 12.4%. Current market (Q4 2024: 154 jobs) represents sustained new equilibrium.

2. Q1 2027 Presents Favorable Entry Timing

All projection scenarios indicate market growth through Q1 2027:

  • Conservative: +14% growth (176 jobs)
  • Moderate: +42% growth (219 jobs)
  • Optimistic: +76% growth (271 jobs)

Even worst-case projections show positive market expansion.

3. Market is Competitive but NOT Oversaturated

Supply-demand ratio of 1.86:1 (moderate scenario) indicates healthy competitive balance. Experienced career switchers with comprehensive credentials face significantly less competition than fresh graduates competing for entry-level positions.

4. Diversified Employer Base Provides Market Resilience

Major employers span healthcare (UPMC, Highmark), financial services (PNC, BNY Mellon), consulting (Deloitte, KPMG), and technology (Google, Duolingo), reducing sector-specific risk.

Portfolio Visualizations

Six analytical visualizations created:

  1. Pittsburgh Job Postings Index: Quarterly Trends (2020-2025)
  2. Estimated DA/BI Job Postings (Scaled, 2020-2024)
  3. Quarter-over-Quarter Growth Rates
  4. Recovery Trajectory from Pandemic Low
  5. Employment Projections with Market Entry Timing (2025-2027)
  6. Market Saturation: Supply vs. Demand Analysis

All visualizations use Pittsburgh brand colors (black/gold) and professional styling suitable for portfolio presentation.

ACT Phase: Recommendations and Next Steps

Strategic Recommendations

1. Proceed with Q1 2027 Job Search Launch

Market projections support February 2027 timing across all economic scenarios. Moderate scenario projects 219 job postings (65 more than current), providing favorable conditions for experienced candidates with comprehensive analytics credentials.

2. Complete Comprehensive Certification Pathway

Mid-level role qualification requires multiple professional certificates (Google Data Analytics, IBM Business Intelligence, Google Advanced Data Analytics) combined with professional experience. Single certificate completion insufficient for mid-level positioning.

3. Emphasize Domain Expertise as Differentiator

15+ years professional experience combined with emerging DA/BI technical skills represents competitive advantage. Position as experienced professional with analytics capabilities rather than junior analyst.

4. Target Diversified Employer Portfolio

Apply across healthcare, financial services, and technology sectors to maximize opportunities and leverage transferable industry knowledge.

5. Monitor Market Indicators Quarterly

Track Indeed index and job posting volumes through Q4 2026 to validate projections and adjust timing if needed. Significant deviation from moderate growth scenario (>10%) would warrant timeline reassessment.

Limitations and Assumptions

Data Limitations:

  • BLS employment data lags current date; 2024-2025 estimates interpolated from historical trends
  • Job posting snapshot represents point-in-time measurement subject to daily fluctuation
  • LinkedIn’s broader BI categorization may slightly inflate estimates
  • Supply estimates based on national data scaled to Pittsburgh; actual local completions may vary

Key Assumptions:

  • DA/BI maintains consistent ~1.4% share of total Pittsburgh job market
  • Economic conditions remain stable (no major recession)
  • AI investment capital translates to hiring activity with 12-18 month lag
  • Skills requirements remain relatively consistent through projection period
  • Comprehensive certification pathway (multiple credentials) positions candidates for mid-level rather than entry-level roles

Scenario Uncertainty:

Wide range between conservative (176 jobs) and optimistic (271 jobs) scenarios reflects genuine economic uncertainty. Moderate scenario represents most probable outcome based on current economic fundamentals.

Future Analysis Opportunities

Potential extensions of this research:

  • Wage trend analysis and salary projection modeling for mid-level roles
  • Detailed skills gap assessment (required vs. available competencies)
  • Geographic comparison (Pittsburgh vs. comparable metros)
  • Investment-to-hiring lag quantification using historical case studies
  • Longitudinal validation (revisit Q1 2027 to assess projection accuracy)

Methodology Appendix

Data Sources Summary

Data Element Source Date Range Sample Size
Job Market Index FRED IHLIDX38300 Q1 2020 - Q4 2024 1,825 daily obs.
DA/BI Job Snapshot LinkedIn/Indeed/Glassdoor Dec 18, 2025 154 postings
Supply Estimates National certificate data 2023-2024 Scaled to Pittsburgh

Scaling Calculation Details

Step 1: Collect multi-platform job posting snapshot (Dec 2025)

  • LinkedIn: 24 DA + 157 BI = 181 postings
  • Indeed: 70 DA + 51 BI = 121 postings
  • Glassdoor: 83 DA + 77 BI = 160 postings
  • Average: (181 + 121 + 160) / 3 = 154 postings

Step 2: Identify corresponding index value

  • Q4 2024 Indeed Index: 107

Step 3: Calculate scaling ratio

  • Ratio: 154 postings / 107 index = 1.44 jobs per index point

Step 4: Apply to historical data

  • Q2 2020 (index 65): 65 × 1.44 = 94 estimated jobs
  • Q4 2024 (index 107): 107 × 1.44 = 154 estimated jobs (validates methodology)

Projection Model Formulas

General Form:
Jobs(t) = Baseline × (1 + growth_rate)^quarters

Where:

  • Baseline = 154 jobs (Q4 2024)
  • growth_rate = scenario-specific quarterly rate
  • quarters = periods from baseline to projection quarter

Example (Moderate Scenario, Q1 2027):
Jobs(Q1_2027) = 154 × (1.04)^9 = 219 jobs

Code Repository

All analysis scripts available in project directory:

  • scripts/01_data_prep.R - FRED data aggregation
  • scripts/02_scale_to_dabi_jobs.R - Scaling methodology
  • scripts/03_historical_analysis.R - Growth rate calculations
  • scripts/04_projections.R - Scenario modeling
  • scripts/05_market_saturation.R - Supply-demand analysis

Reproducibility: All code uses relative file paths and documented workflows. Analysis can be replicated by running scripts in sequence with provided data files.

References

Data Sources:

  • Federal Reserve Bank of St. Louis. (2025). Indeed Job Postings Index: Pittsburgh, PA (MSA) [IHLIDX38300]. FRED Economic Data. https://fred.stlouisfed.org/series/IHLIDX38300

  • LinkedIn, Indeed, Glassdoor. (2025). Job posting data collected December 18, 2025.

Methodological References:

  • U.S. Bureau of Labor Statistics. (2024). Occupational Employment and Wage Statistics. https://www.bls.gov/oes/

  • Google Career Certificates. (2024). Enrollment and completion data.

Analysis completed December 20, 2025
Google Data Analytics Professional Certificate Capstone Project