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Senior Data Engineer, Data Engineering, Contact Center Analytics, SQL, ETL/ELT, Power BI, AWS, API Integrations, Team Leadership
Assurant. Design and maintain ETL/ELT data pipelines transforming high-volume event and interaction data into analytics-ready datasets .
Tech Stack
Tools & technologiesETLSQLGo
About the role
Key responsibilities & impact- Design and maintain ETL/ELT data pipelines transforming high-volume event and interaction data into analytics-ready datasets
- Build and optimize SQL-based data models, including star schemas and semantic layers for reporting
- Develop and maintain standardized KPIs and metrics for operational dashboards
- Support Power BI datasets and reports, ensuring consistency between data models and visualizations
- Investigate production data issues and partner with engineering teams during releases and incidents
- Define metric definitions, documentation, and data quality checks to support data governance
- Build supporting services and tooling using Go or similar backend languages
- Collaborate with product, engineering, and analytics partners to deliver trusted datasets, scalable pipelines, and well-defined metrics
Requirements
What you’ll need- 6+ years of experience in data engineering or analytics engineering
- Strong SQL skills and experience designing dimensional (star) schemas
- Hands-on experience building and operating ETL/ELT pipelines
- Experience supporting BI tools such as Power BI
- Proficiency in Go (preferred) or another backend programming language
- Experience working in production environments with real-time or operational data
- Experience with contact center, IVR, or digital interaction data
- Familiarity with data governance, metric standardization, or semantic modeling
- Experience supporting automation or AI-driven products
- Work timing availability from 3:30 PM IST to 12:30 AM IST
- Employment is contingent upon completion of a required identity verification process
Benefits
Comp & perks- High ownership role with real influence on data architecture and standards
- Collaborative, product-focused engineering culture
- Work on data that directly impacts customer experience and operational performance
- Video-conducted virtual interviews
- Required identity verification process, which may include biometric technology where permitted by law