Lead Developer - GIS - Pega
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Location
In office (Sydney)
Seniority
Staff
Confirmed on the employer's own hiring board on Oct 30, 2026. First seen by Alion on Oct 29, 2026.
Overview
Company
Impact
Profile match
Tech Mahindra is a prominent Indian multinational information technology services and consulting enterprise specializing in digital transformation, cloud modernization, business process re-engineering, and network services. Headquartered in Pune, Maharashtra, India (with its registered office in Mumbai), the company is a flagship entity of the diversified Mahindra Group conglomerate. Established in 1986 (initially as Mahindra British Telecom), it operates across dozens of countries with a global delivery footprint serving enterprise clients in telecommunications, banking and financial services, healthcare, automotive, and retail. The organization is particularly recognized for its deep engineering heritage in telecommunications and 5G network integration, offering end-to-end solutions spanning artificial intelligence, enterprise application management, customer experience (CX), and next-generation connectivity infrastructure.
Backed by Seafund
Job Description
Skill Set :
Pega Decisioning Consultant (PCDC)
Total Experience :
12.00 to 12.00 Years
No of Openings :
1
Job Post Date :
08/10/2026
Job Expiry Date :
31/10/2026
Domain :
IT
Location :
SYDNEY [Australia]
Job Reference No :
4111883
Job Summary
- Design and develop customer engagement solutions using Pega CDH.
- Configure Next Best Action Designer (NBAD) components including:
- Engagement Policies
- Contact Policies
- Arbitration
- Channel Strategies
- Action Eligibility
- Build and maintain decision strategies and decision tables.
- Configure customer journeys and customer engagement programs.
- Monitor strategy execution performance and response times.
- Analyze and optimize:
- NBA processing performance
- Strategy performance
- Data flow performance
- Campaign execution times
- Customer interaction processing
- Identify bottlenecks impacting customer decisioning.
- Optimize decision strategies by reducing:
- Complex joins
- Excessive database calls
- Redundant components
- Unnecessary adaptive model executions
- Perform rule and database performance tuning.
- Review system alerts and PAL statistics.
- Support performance testing activities for large customer volumes.
- Ensure scalability for millions of customer decisions and interactions.
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