Lead Data Engineer (DBT, Databricks, Azure) at encora10

Language

Location: São Paulo, São Paulo
Level: lead/mgmt
Posted: Aug 31, 2026

Description

Coforge is hiring a Lead Data Engineer to lead the RTS delivery squad and own end-to-end delivery of strategic data products on a modern cloud data platform. The role covers low-latency API-driven data solutions, Python backend services, Azure Databricks and DBT pipelines, data modeling, Terraform infrastructure, CI/CD, observability, and team leadership. Candidates need 8+ years of relevant technical experience, including at least 3 years leading engineering teams, and the position is open to candidates across LATAM.

Required Skills

Python
REST APIs
Azure Databricks
DBT
SQL
Terraform
Data Engineering
Data Modeling
CI/CD
Team Leadership

Additional Information

Required Previous Experiences: 8+ years of experience in Data Engineering, Software Engineering, or related technical roles
At least 3 years of experience leading engineering teams
Backend development and production-grade REST API delivery
PyTest, unit testing, automated testing, and software quality practices
Azure Databricks and DBT experience
Advanced SQL and scalable data solutions
Data modeling and consumption-oriented architectures
Terraform and Infrastructure as Code deployments
Low-latency data products and API-driven solutions
CI/CD, Git, and modern software engineering practices
Technical delivery leadership with hands-on architecture, development, and code reviews
Preferred Qualifications: Experience in insurance, financial services, or other highly regulated industries
Insurance concepts including policy administration, claims, underwriting, and customer data
Snowflake and Snowflake-to-Databricks integration or migration patterns
Azure AI Search, Lakebase, feature stores, or similar low-latency serving technologies
Streaming and real-time data technologies such as Kafka, Event Hubs, or Delta Live Tables
Data governance, security, and compliance frameworks
Large-scale DBT environments and terabyte-scale data pipelines
Cloud migration initiatives, particularly AWS-to-Azure migrations
Machine learning platforms and collaboration with ML engineering teams