Top Data Engineer Jobs in Leon

Develop and execute manual and automated tests for data-focused applications, APIs, databases, and pipelines. Translate requirements into test plans, validate data quality and integrity, track defects, collaborate with engineering and stakeholders, and improve testing processes, automation, coverage, and efficiency using AI tools.

Design, build, and optimize scalable data pipelines and storage on Azure (data lakes/warehouses). Integrate and transform data from databases, APIs, and streaming sources; tune performance; collaborate with data scientists and stakeholders; work in Agile; mentor junior engineers and deliver robust, production-ready solutions with CI/CD practices.

Designs, develops, and maintains scalable data pipelines and models in Databricks and Microsoft Azure. Responsibilities include data ingestion, transformation, integration, performance optimization, troubleshooting, migration, governance, documentation, and collaboration with architecture and engineering teams. The role requires translating business needs into reliable technical data solutions and supporting analytical and operational use cases.

Build and maintain secure, scalable data pipelines and platform components using AWS, Snowflake, dbt, and Airflow. Modernize legacy data flows, integrate batch and streaming sources, improve testing and observability, and support self-service analytics. Develop AI-enabled capabilities such as anomaly detection and automated tagging. Partner with Finance, Risk, Product, Analytics, ML, and Engineering teams to define requirements, meet SLAs, ensure data governance, and deliver reliable infrastructure end to end.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Design, build, and operationalize scalable data solutions for customers; support data ingestion, transformation, quality, governance, and analytics to enable AI/ML and BI. Troubleshoot, test pipelines, collaborate with teams, and guide customers on technical decisions.

The Senior Software Engineer will design and operate data ingestion and processing systems for multimodal data, ensuring quality, security, and compliance while optimizing for performance and reliability. Responsibilities include building pipelines, handling messy data, and partnering with teams to enhance the platform's capabilities.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and build self-service analytics portals and responsive dashboards. Integrate data pipelines from Databricks, S3, Salesforce and internal APIs. Implement secure auth and role-based access. Collaborate with product and data teams, remain hands-on, and mentor junior developers to deliver scalable, customer-facing analytics experiences.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

The Data Quality Engineer ensures the quality and integrity of data in studies, developing measurement systems, automation, and conducting analysis to uphold standards across projects.

Senior integration engineer responsible for designing and implementing API integrations, ETL pipelines, and Power Platform connections; building and maintaining APIs and data transformation workflows; ensuring data quality, security, reliability, and optimizing/troubleshooting integration flows.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

The Staff Engineer, Data Science role focuses on leveraging tech skills to enhance team capabilities and provide advanced solutions for client challenges.

Design, own, and scale the enterprise LookML semantic layer powering internal and client analytics. Build performant, reusable Looker data products, optimize queries with Analytics Engineering, enforce LookML style and governance, mentor junior developers, and enable conversational/AI-driven analytics across multiple Looker instances.

Design and maintain end-to-end data pipelines using Microsoft Fabric and Azure Synapse, ensuring data quality and performance optimization while collaborating with cross-functional teams.

Design and maintain data pipelines, optimize data processing with PySpark on Databricks, ensure data quality, and collaborate on data architecture solutions.

Drive quality assurance strategy for data migration projects, validating data integrity and automation using AWS technologies and Python scripts.

Lead the migration of SQL Server and PostgreSQL to Azure SQL. Design and support PaaS and DBaaS offerings while optimizing database instances.

Lead design, build, and optimize scalable data architectures and end-to-end ETL pipelines. Ensure data quality, governance, performance monitoring, mentor engineers, and collaborate with cross-functional teams to deliver reliable data platforms.

Support design, build, and maintain AWS-based ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions), S3/Redshift/RDS/Oracle, write SQL, implement monitoring/logging, ingest API/JSON data, and assist deployment, documentation, and code management.

Design, build, and maintain ETL pipelines and near-real-time data systems on AWS using Python and PySpark. Orchestrate workflows, implement event-driven integrations, optimize storage/querying (Redshift, RDS, S3), enforce data quality and monitoring, participate in CI/CD, and collaborate with stakeholders to deliver data solutions.