Lead II - Data Engineering - Data Governance

UST; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

UST in Bengaluru is seeking a hands-on Data Engineer to build scalable, AWS-native data pipelines and event-driven architectures for an annuity reinsurance platform. This role focuses on canonical data models, integration across policy administration, reinsurance, and finance, plus reporting and AI capabilities.

The position emphasizes data quality, governance, lineage, and CI/CD for data solutions, requiring 5+ years in data engineering and strong AWS data stack skills.

Qualifications

• 5+ years of Data Engineering experience.
• Experience building AWS-based cloud-native data solutions.
• Strong understanding of modern data architecture and integration patterns.
• Experience supporting large-scale enterprise data platforms.

Responsibilities

• Design and maintain canonical data models for contracts, transactions, funds, treaties, and reporting.
• Develop batch, API, and event-driven ingestion frameworks.
• Build scalable data structures and ensure data quality, lineage and governance.
• Implement data pipelines using AWS services like S3, Glue, Redshift, DynamoDB, Lambda, EventBridge, SQS, and Step Functions.
• Support reporting, analytics, and AI readiness with auditable datasets.

Skills

Python

SQL

Java

Scala

TypeScript

PostgreSQL

Aurora PostgreSQL

DynamoDB

Redshift

Snowflake

OpenSearch

Apache Kafka

Airflow

Terraform

Docker

Kubernetes

Data Vault

DDD

Event Sourcing

CQRS

Education

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field

Tools

PostgreSQL

Aurora PostgreSQL

DynamoDB

MongoDB

Redshift

Snowflake

OpenSearch

ETL/ELT tooling

OpenSearch

AWS Data Stack

Job description

Role Description

Data Engineer AWS-Native, Event-Driven Annuity Reinsurance Platform Role Overview We are seeking a Data Engineer to build and support the data foundation of a next-generation AWS-native annuity reinsurance platform. This role will focus on scalable data pipelines, canonical data models, event-driven processing, reporting datasets, audit repositories, and integrations supporting contracts, transactions, funds, treaties, cessions, billing, analytics, and future AI capabilities. Key Responsibilities Data Engineering & Architecture Design and maintain canonical data models for contracts, transactions, funds, treaties, cessions, billings, and reporting. Define data standards, metadata, governance, lineage, and source-to-target mappings. Build scalable operational and analytical data structures. Data Pipeline Development Develop batch, API, and event-driven ingestion frameworks. Integrate data from policy administration, reinsurance, finance, TPA, partner, and market data systems. Implement transformation, validation, reconciliation, error handling, and recovery processes. AWS Data Engineering Design and implement solutions using: S3, Glue, Redshift Aurora PostgreSQL, DynamoDB Lambda, EventBridge, SNS, SQS, Step Functions Lake Formation, DataZone, OpenSearch Ensure scalability, security, compliance, and operational efficiency. Event-Driven Processing Support real-time data flows through: Event schema design Event publishing and consumption Replay capabilities Dead-letter management Idempotent processing Data Quality & Governance Implement data quality frameworks and validation rules. Support reconciliation, duplicate detection, lineage, and audit tracking. Manage reference and master data controls. Reporting & Analytics Build datasets supporting: Placement, billing, cession, and operational reporting Audit and compliance reporting KPI dashboards and trend analysis AI Readiness Support AI-enabled capabilities through: Searchable enterprise datasets Metadata enrichment Document indexing RAG readiness AI data quality monitoring DevOps & Operations Build CI/CD pipelines for data solutions. Automate testing and deployments. Monitor pipelines and troubleshoot production issues. Support operational SLAs and reliability goals. Required Qualifications Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field. 5+ years of Data Engineering experience. Experience building AWS-based cloud-native data solutions. Strong understanding of modern data architecture and integration patterns. Experience supporting large-scale enterprise data platforms. Technical Skills Programming Python SQL Java, Scala, or TypeScript (preferred) Databases PostgreSQL / Aurora PostgreSQL SQL Server DynamoDB MongoDB Redshift Snowflake (preferred) AWS Data Stack S3, Glue, Lambda EventBridge, SNS, SQS, Step Functions Redshift IAM, CloudWatch Data Integration REST APIs Event-driven integrations ETL/ELT pipelines File ingestion frameworks Data Governance Data lineage Metadata management Data catalogs Data security and retention Auditability Master Data Management Preferred Domain Experience Life Insurance Annuity Administration Reinsurance Administration Financial Services Policy Administration Systems Knowledge of treaty reinsurance, cessions, billing & settlement, financial reporting, and annuity products is highly desirable. Preferred Skills Apache Kafka OpenSearch / Elasticsearch dbt Airflow Terraform Docker & Kubernetes Data Vault Modeling Domain-Driven Design (DDD) Event Sourcing CQRS Desired Competencies Strong analytical and troubleshooting skills Data-first mindset with focus on quality and governance Strong communication and collaboration skills Attention to detail and auditability Ability to thrive in fast-paced product environments Success Measures Deliver scalable canonical data models and ingestion frameworks. Ensure high-quality, auditable, and traceable data. Enable accurate reinsurance placement, cession, and billing processes. Build reusable data services for multi-client deployments. Establish a foundation for reporting, analytics, and AI capabilities. Ideal Candidate A hands-on Data Engineer with expertise in AWS cloud data engineering, event-driven architectures, enterprise data platforms, and insurance/reinsurance data processing, capable of building scalable, production-grade data solutions for a modern annuity reinsurance platform.