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

KOUD; United States; Hybrid; USD 140,000 - 190,000; Full time

Job summary

KOUD is seeking a Senior Data Engineer to design, build, and optimize modern data platforms enabling AI, analytics, and BI across enterprise systems. The role emphasizes Databricks, Delta Lake, and cloud-native architectures, with technical leadership to set standards and mentor engineers.

The ideal candidate will drive data quality, governance, and scalable pipelines using Spark, PySpark, and related tooling, while collaborating with data science teams to operationalize ML solutions.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
• Strong experience in data engineering, data warehousing, or data platform development.
• 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.

Responsibilities

• 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.
• Design 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 IaC for Databricks deployments.
• Design cloud-native data architectures across Azure and AWS environments.
• Integrate enterprise data sources including SQL Server, Oracle, APIs, SaaS 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.
• Build and optimize data products supporting AI, ML, and generative AI workloads.
• Develop feature engineering pipelines and curated datasets for model training and inference.
• Support vector databases, semantic search, RAG, and LLM-powered applications.
• Collaborate with Data Scientists and AI Engineers to operationalize ML solutions.
• Implement MLOps and DataOps practices to support scalable AI delivery.

Skills

Databricks

Apache Spark

Delta Lake

Unity Catalog

Python

SQL

Data Modeling

MLOps

Cloud: Azure/AWS

CI/CD

IaC

Data Warehousing

Education

Bachelor's degree in CS/IT/Engineering

Master's degree (preferred)

Tools

Databricks Workflows

Delta Live Tables

Terraform

GitHub

Azure DevOps

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, SaaS 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 DataOps 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 (PySpark, Spark SQL), Delta Lake, Delta Live Tables, Unity Catalog, Data Warehousing, and Data Modeling;
• Programming: Python, SQL, Scala, PowerShell, and scripting automation;
• Cloud: Microsoft Azure, AWS, and cloud-native storage and compute services;
• Databases: SQL Server, Oracle, PostgreSQL, and Snowflake;
• Data Integration: Azure Data Factory, Databricks Workflows, Kafka/Event Streaming, and REST APIs;
• DevOps & Automation: GitHub, Azure DevOps, CI/CD pipelines, Terraform, and Infrastructure as Code (IaC);
• 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:

• Hybrid position, with 3 days per week working on-site in Pinheiros, São Paulo.