- Key Responsibilities Data Pipeline Engineering Design, build, and maintain scalable data pipelines ingesting insurance operational data (policy, supporting datasets such as employee details, CRM activities, training data) Develop and optimize ETL/ELT workflows across structured (SQL) and semi-structured (JSON/MongoDB) data sources Ensure pipeline reliability, scheduling, monitoring, and alerting with minimal manual intervention Insurance
- Domain Data Modelling Translate complex incentive structures and payout workflows, including agent commissions, incentives into clean, normalized data models Document data dictionaries, schema definitions, and transformation logic for client-facing deliverables Analytics Enablement Prepare clean, analytics-ready datasets for dashboards, actuarial models, and management reporting Support ad-hoc analytical requests with SQL queries, MongoDB aggregations, and scripting (Python/JavaScript)
- System Integrations Integrate the incentive output with downstream systems accounting/GL platforms, payroll, and HRIS including journal entry generation (CGST/SGST/IGST handling where applicable) and payout file generation

- Build and support workflow requirements to fulfill business requirements related to downstream data consumption Client Engagement Act as the primary technical point of contact for data-related queries from the dedicated client Communicate clearly with both technical and non-technical insurance stakeholders Produce regular status updates, pipeline documentation, and data health reports Insurance Domain Knowledge A solid understanding of insurance operations is preferred.
- The ideal candidate will be familiar with: Core insurance lines: PC, Life, Health, or Specialty (at least one in depth) Key operational data areas: Incentive management, commission payouts, payout reconciliation,



and incentive disbursement Working with real-world insurance data from legacy source systems Who You Are When you think about data, you naturally ask ;
- Do I completely understand each and every data field present in this datasets; and How do multiple datasets link together and what would it allow me to answer You are technically hands-on and comfortable writing and debugging code independently You can read a business process, identify its data footprint, and model it cleanly You communicate complex technical findings to non-technical insurance stakeholders with clarity
- You thrive in a dedicated client model, you build trust, take ownership, and deliver consistently You are detail-oriented with a high bar for data accuracy and documentation quality You Mentor junior data engineers and consultants on the account; review pipeline code and enforce data quality and auditability standards.
- Qualifications 3 6 years of experience in data engineering, analytics engineering, or a related data role Good to Have: Prior experience in the insurance industry Bachelors or Masters degree in Computer Science, Statistics, Engineering, Mathematics, or a related field Strong SQL skills are non-negotiable; MongoDB
- proficiency is highly preferred Experience with Python or JavaScript for data processing; both is a plus Excellent written and verbal communication skills in English Based in Chennai or willing to relocate, this is an on-site, dedicated engagement role Nice to Have Experience with BI tools (Power BI, Tableau, Looker)
- for data validation and stakeholder reporting Familiarity with cloud data platforms (AWS, GCP, Azure) and data warehousing solutions Knowledge of actuarial data workflows or experience supporting actuarial teams Certifications in cloud, data engineering, or insurance domain (ACII, AICPCU, etc.)

📌 Data Consultant (Chennai)
🏢 Kennect Solutions
📍 Chennai