Top Data Engineer Jobs in Leon

The Data Architect role involves defining the data strategy, optimizing data schemas, enhancing performance, and collaborating with multiple teams for effective data management.

The Data Engineer I will design and manage data pipelines, integrate ETL processes, ensure data quality, and optimize performance while working with various cloud and on-premises technologies.

The Azure Data Engineer will participate in the software development lifecycle, including design, coding, testing, and documentation of applications, collaborating closely with team members and following best practices.

Lead AWS Data Engineer responsible for designing and implementing cloud-native data solutions, overseeing data migrations, and mentoring engineering teams.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

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.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python and PySpark. Develop Glue/Lambda/Step Functions workflows, event-driven integrations, and data lake/warehouse solutions. Implement data validation, monitoring, and error-handling; tune SQL and performance; support APIs/JSON integrations; troubleshoot production issues; document, automate deployments, and mentor junior engineers.

Support design, build, and maintenance of ETL pipelines using Python and PySpark. Develop workflows with AWS Glue, Lambda, and Step Functions. Work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, validation, and reporting. Implement monitoring, logging, and error handling. Ingest/process API and JSON payloads. Collaborate with engineers, analysts, and stakeholders; contribute to code management, documentation, and deployment support.

Support migration of archived Concur data to AWS S3 using AWS Glue Catalog and Amazon Athena. Upload and organize data in S3, create Glue Catalog tables from DDL, configure Athena, develop and run SQL queries for data retrieval and validation, interpret relational schemas, join and validate datasets, and perform basic file handling, extraction, and structured data analysis.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Build and maintain end-to-end AWS data pipelines: design reusable Python packages, implement ETL/Orchestration (Airflow), validate data with DBT/tests, optimize SQL for Snowflake/Redshift, integrate cloud services (S3, Lambda, EMR), implement CI/CD in GitHub, and monitor pipelines (CloudWatch, DataDog). Client-facing, hands-on individual contributor with strong troubleshooting and documentation responsibilities.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

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.

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.

Manage the Databricks environment, implement data governance, oversee data architecture in Snowflake, and handle data pipelines across cloud platforms.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

Build, automate, and manage end-to-end data ingestion pipelines, ensure integrations with downstream systems like ServiceNow, use Snowflake for storage and OpenTelemetry for observability, implement data validation and troubleshoot pipeline failures to maintain data integrity.

Implement medallion (bronze/silver/gold) architecture on a Snowflake stack, migrate/schedule ETL into Airflow DAGs, harden pipelines with automated testing, alerting, and failure recovery, audit and optimize dbt code and pipeline costs, maintain Paradime-Airflow integrations, and ensure zero downtime for downstream BI during infrastructure changes.

Hands-on Senior Data Engineer to design and maintain scalable AWS ETL/ELT pipelines, evolve a data lakehouse with Apache Iceberg, implement governance and security, optimize performance and costs, enable analytics-ready datasets, and ensure reliability, monitoring, and data quality.

Design and build scalable data pipelines and models for research laboratory instrument outputs. Implement custom parsers, unit/integration tests, modular utilities, and dashboards (Streamlit/Tableau/Jupyter). Perform comparative dataset analysis and collaborate with product and engineering to deliver AI-enabled data solutions.

Design, build, and operationalize scalable data solutions and pipelines for customers. Analyze and transform data, ensure data quality/governance/security, test data movements, troubleshoot incidents, and collaborate with teams to support AI/ML and BI use cases.

Design, build, and scale data pipelines and analytics solutions across the full data lifecycle. Implement ETL, data modeling, and high-volume data systems using Spark/Scala, Python, Databricks, Airflow, and AWS. Collaborate with stakeholders, contribute to architecture and CI/CD practices, and deliver projects on schedule.

Build, monitor, and troubleshoot data pipelines and transformations for healthcare data. Extract, cleanse, and load data; implement data quality checks; document datasets; support API and EFT integrations; triage incidents; and participate in Agile ceremonies to estimate and deliver data solutions.

The Senior Databricks Engineer will design, build, and maintain scalable data pipelines, optimize ETL/ELT processes, and ensure data quality and governance while collaborating with teams to meet data requirements.