Senior Data Engineer (Databricks & Ai-Enabled Data Platforms)

Koud; São Paulo; On-site; BRL 210,000 - 350,000; Full time

Job summary

Koud is seeking a Senior Data Engineer to design, build, and optimize modern data platforms enabling analytics, AI/ML, and BI solutions. You will lead data engineering efforts on Databricks, Delta Lake, and cloud-native architectures across Azure and AWS.

The role requires deep expertise in data warehousing, data modeling, and governance, with guidance to junior engineers and a focus on scalable, secure data solutions for enterprise initiatives.

Qualifications

• Bachelor’s degree in Computer Science, IT, Engineering or related field.
• Hands-on experience with Databricks and Apache Spark.
• Experience designing and implementing enterprise-scale cloud data platforms.
• Proficiency in Python, SQL, Scala, and scripting.
• Cloud familiarity with Azure and AWS; data governance and security awareness.

Responsibilities

• Design, build, and maintain scalable data pipelines on Databricks and cloud-native tech.
• Develop data quality, observability, lineage, and governance across data platforms.
• Lead technical initiatives and mentor engineers; establish engineering standards.
• Implement CI/CD and Infrastructure-as-Code for Databricks deployments.

Skills

Databricks

Apache Spark

Python

SQL

Delta Lake

Unity Catalog

ETL/ELT pipelines

Azure

AWS

CI/CD

IaC

MLOps

Education

Bachelor’s degree in Computer Science/IT/Engineering

Tools

Terraform

Azure DevOps

GitHub

Databricks Workflows

Job description

Koud is looking for a Senior Data Engineer (Databricks & AI-Enabled Data Platforms) to work with one of our clients. We are seeking a highly skilled Senior Data Engineer to design, build, and optimize modern data platforms that enable advanced analytics, AI/ML, and business intelligence solutions. The ideal candidate will possess deep expertise in Databricks, cloud-based data engineering, data architecture, and modern software engineering practices. This role will provide technical leadership, establish engineering standards, and drive the development of scalable, secure, and reliable data solutions supporting enterprise-wide initiatives.

Key Responsibilities:

Data Platform Engineering: Design, develop, and maintain scalable data pipelines using Databricks, Spark, and cloud-native technologies;Build and optimize batch and real-time data ingestion frameworks;Develop reusable engineering patterns, frameworks, and accelerators for enterprise data solutions;Implement data quality, observability, lineage, and governance capabilities across the data ecosystem;Support large-scale data modernization and cloud migration initiatives.

Databricks Engineering: Design and implement Delta Lake architectures and Medallion data models;Build optimized ETL/ELT pipelines using Databricks notebooks, workflows, and Delta Live Tables;Establish performance tuning, cost optimization, and workload management best practices;Leverage Unity Catalog for data governance, security, and metadata management;Implement CI/CD pipelines and Infrastructure-as-Code for Databricks deployments.

Cloud Data Engineering: Design cloud-native data architectures across Azure and AWS environments;Integrate enterprise data sources including SQL Server, Oracle, APIs, Saa S platforms, and streaming platforms;Build resilient and secure data services supporting high availability and disaster recovery requirements;Partner with infrastructure and security teams to implement cloud security controls and compliance standards. AI and Advanced Analytics Enablement: Build and optimize data products supporting AI, machine learning, and generative AI workloads;Develop feature engineering pipelines and curated datasets for model training and inference;Support vector databases, semantic search, RAG (Retrieval-Augmented Generation), and LLM powered applications;Collaborate with Data Scientists and AI Engineers to operationalize machine learning solutions;Implement MLOps and Data Ops practices to support scalable AI delivery.

Technical Leadership: Serve as a technical lead for complex data engineering initiatives;Define architecture standards, engineering best practices, and development guidelines;Mentor junior and mid-level engineers;Conduct architecture reviews and provide technical recommendations;Drive adoption of automation, observability, and platform engineering principles.

Required

Qualifications / Skills: Bachelor's degree in Computer Science, Information Technology, Engineering, or related discipline;Strong experience in data engineering, data warehousing, or data platform development;Years of hands‐on experience with Databricks and Apache Spark;Experience designing and implementing enterprise‐scale cloud data platforms;Experience leading technical initiatives and mentoring engineering teams;Data Engineering: Databricks, Apache Spark (Py Spark, Spark SQL), Delta Lake, Delta Live Tables, Unity Catalog, Data Warehousing, and Data Modeling;Programming: Python, SQL, Scala, Power Shell, and scripting automation;Cloud: Microsoft Azure, AWS, and cloud-native storage and compute services;Databases: SQL Server, Oracle, Postgre SQL, and Snowflake;Data Integration: Azure Data Factory, Databricks Workflows, Kafka/Event Streaming, and REST APIs;Dev Ops & Automation: Git Hub, Azure Dev Ops, CI/CD pipelines, Terraform, and Infrastructure as Code (Ia C);Observability: Monitoring, alerting, data quality, performance optimization, logging, and troubleshooting;AI & Emerging Technologies: Machine Learning pipelines, Feature Stores, MLOps, Generative AI, Vector Databases, RAG, and AI Governance;English Fluent.

Preferred:

Master's degree preferred.

Databricks Certified Data Engineer Professional;Azure Data Engineer Associate certification;AWS Data Analytics certification;Experience supporting regulated industries such as Insurance, Financial Services, or Healthcare;Experience implementing enterprise data governance programs;Experience integrating AI solutions into production environments.

Additional Information: