Lead Data Engineer (DBT, Databricks, Azure)
Reads like a Data Leadership role: running a data team.
Latin America
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingLead the RTS delivery squad and own end-to-end delivery of data products
Design and build data pipelines using Azure Databricks, DBT and SQL
Build Python REST APIs and backend services for low-latency data serving
Manage infrastructure deployment with Terraform and Infrastructure as Code
Mentor and coach a team of data and machine learning engineers
From the employer’s posting
Lead the RTS delivery squad and own the end-to-end delivery of strategic data products
Design, develop, and optimize data pipelines using Azure Databricks, DBT, SQL, and cloud-native technologies
Build and maintain scalable backend services and REST APIs using Python and object-oriented programming principles
Lead infrastructure deployment and management using Terraform and Infrastructure as Code (IaC) practices
Mentor and develop a team of data and machine learning engineers
What you’ll bring
All qualificationsPreferred experience
- Experience within insurance, financial services, or other highly regulated industries.
Qualification wording
Experience within insurance, financial services, or other highly regulated industries. • Knowledge of insurance concepts such as policy administration, claims, underwriting, and customer data. • Experience with Snowflake and Snowflake-to-Databricks integration or migration patterns. • Hands-on experience with Azure AI Search, Lakebase, feature stores, or similar low-latency serving technologies. • Experience with streaming and real-time data technologies such as Kafka, Event Hubs, or Delta Live Tables. • Experience implementing data governance, security, and compliance frameworks. • Experience managing large-scale DBT environments and terabyte-scale data pipelines. • Experience leading cloud migration initiatives, particularly AWS-to-Azure migrations. • Familiarity with machine learning platforms and collaboration with ML engineering teams.
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- dbt
- Delta Lake
- Kafka
- Snowflake
- Terraform
Source — Tool mentions in context
Job Title: Lead Data Engineer (DBT, Databricks, Azure) Key Skills: Python, REST APIs, Azure Databricks, DBT, SQL, Terraform, Data Engineering, Data Modeling, CI/CD, Team Leadership Experience: 8+ years of experience.
Responsibilities • Lead the RTS delivery squad and own the end-to-end delivery of strategic data products within a modern cloud data platform. • Design and oversee the architecture of low-latency, API-driven data solutions, from backend data models through serving layers and API consumption. • Build and maintain scalable backend services and REST APIs using Python and object-oriented programming principles. • Design, develop, and optimize data pipelines using Azure Databricks, DBT, SQL, and cloud-native technologies. • Own the architecture and implementation of curated data layers, ensuring high availability, scalability, and performance. • Evaluate, select, and implement technologies for low-latency data serving and API consumption. • Lead infrastructure deployment and management using Terraform and Infrastructure as Code (IaC) practices. • Mentor and develop a team of data and machine learning engineers, providing technical guidance, coaching, and career development support. • Conduct code reviews and establish engineering standards, development best practices, and architectural guidelines. • Collaborate with Product, Engineering, Business, and Project stakeholders to define technical solutions and delivery roadmaps. • Ensure data quality, observability, monitoring, alerting, and SLA compliance across all data products and services. • Drive CI/CD adoption and oversee release management processes for pipelines, services, and platform components. • Act as the primary technical escalation point for troubleshooting complex production issues and ensuring long-term platform stability. • Maintain comprehensive technical documentation, architectural decisions, and implementation standards. Mandatory Requirements
Mandatory Requirements • 8+ years of experience in Data Engineering, Software Engineering, or related technical roles. • At least 3 years of experience leading engineering teams, including senior, mid-level, and junior professionals. • Strong backend development experience with proven delivery of production-grade REST APIs and backend services. • Expert-level programming skills in Python, including strong object-oriented programming (OOP) principles. • Extensive experience with PyTest, unit testing, automated testing frameworks, and software quality practices. • Strong hands-on experience with Azure Databricks and DBT. • Advanced SQL skills and experience building scalable data solutions. • Deep understanding of data modeling, dimensional modeling, and consumption-oriented data architectures. • Extensive experience with Terraform and Infrastructure as Code (IaC) deployments. • Experience designing and delivering low-latency data products, serving layers, or API-driven data solutions. • Strong knowledge of CI/CD pipelines, Git-based version control, and modern software engineering practices. • Proven ability to lead technical delivery while remaining hands-on with architecture, development, and code reviews. • Excellent communication and stakeholder management skills, with the ability to work effectively across technical and business teams. Preferred Requirements
Preferred Requirements • Experience within insurance, financial services, or other highly regulated industries. • Knowledge of insurance concepts such as policy administration, claims, underwriting, and customer data. • Experience with Snowflake and Snowflake-to-Databricks integration or migration patterns. • Hands-on experience with Azure AI Search, Lakebase, feature stores, or similar low-latency serving technologies. • Experience with streaming and real-time data technologies such as Kafka, Event Hubs, or Delta Live Tables. • Experience implementing data governance, security, and compliance frameworks. • Experience managing large-scale DBT environments and terabyte-scale data pipelines. • Experience leading cloud migration initiatives, particularly AWS-to-Azure migrations. • Familiarity with machine learning platforms and collaboration with ML engineering teams. Published on: 25-08-2026
Job Title: Lead Data Engineer (DBT, Databricks, Azure) Key Skills: Python, REST APIs, Azure Databricks, DBT, SQL, Terraform, Data Engineering, Data Modeling, CI/CD, Team Leadership
Posting record
Report an errorOpen 3 monthsWe first saw this listing 106 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 7, 2026 · 106 days ago
- Recorded sightings
- 154
- Last seen by us
- Oct 21, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
Responsibilities
• Design and oversee the architecture of low-latency, API-driven data solutions, from backend data models through serving layers and API consumption.
• Build and maintain scalable backend services and REST APIs using Python and object-oriented programming principles.
• Design, develop, and optimize data pipelines using Azure Databricks, DBT, SQL, and cloud-native technologies.
• Own the architecture and implementation of curated data layers, ensuring high availability, scalability, and performance.
• Evaluate, select, and implement technologies for low-latency data serving and API consumption.
• Lead infrastructure deployment and management using Terraform and Infrastructure as Code (IaC) practices.
• Mentor and develop a team of data and machine learning engineers, providing technical guidance, coaching, and career development support.
• Conduct code reviews and establish engineering standards, development best practices, and architectural guidelines.
• Collaborate with Product, Engineering, Business, and Project stakeholders to define technical solutions and delivery roadmaps.
• Ensure data quality, observability, monitoring, alerting, and SLA compliance across all data products and services.
• Drive CI/CD adoption and oversee release management processes for pipelines, services, and platform components.
• Act as the primary technical escalation point for troubleshooting complex production issues and ensuring long-term platform stability.
• Maintain comprehensive technical documentation, architectural decisions, and implementation standards.
Mandatory Requirements
• At least 3 years of experience leading engineering teams, including senior, mid-level, and junior professionals.
• Strong backend development experience with proven delivery of production-grade REST APIs and backend services.
• Expert-level programming skills in Python, including strong object-oriented programming (OOP) principles.
• Extensive experience with PyTest, unit testing, automated testing frameworks, and software quality practices.
• Strong hands-on experience with Azure Databricks and DBT.
• Advanced SQL skills and experience building scalable data solutions.
• Deep understanding of data modeling, dimensional modeling, and consumption-oriented data architectures.
• Extensive experience with Terraform and Infrastructure as Code (IaC) deployments.
• Experience designing and delivering low-latency data products, serving layers, or API-driven data solutions.
• Strong knowledge of CI/CD pipelines, Git-based version control, and modern software engineering practices.
• Proven ability to lead technical delivery while remaining hands-on with architecture, development, and code reviews.
• Excellent communication and stakeholder management skills, with the ability to work effectively across technical and business teams.
Preferred Requirements
• Knowledge of insurance concepts such as policy administration, claims, underwriting, and customer data.
• Experience with Snowflake and Snowflake-to-Databricks integration or migration patterns.
• Hands-on experience with Azure AI Search, Lakebase, feature stores, or similar low-latency serving technologies.
• Experience with streaming and real-time data technologies such as Kafka, Event Hubs, or Delta Live Tables.
• Experience implementing data governance, security, and compliance frameworks.
• Experience managing large-scale DBT environments and terabyte-scale data pipelines.
• Experience leading cloud migration initiatives, particularly AWS-to-Azure migrations.
• Familiarity with machine learning platforms and collaboration with ML engineering teams.
Source details
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- Location & working pattern
Latin America
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