Top Data Engineer Jobs in Leon

Build and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed medallion-architecture Lakehouses, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls with Unity Catalog. Troubleshoot production incidents, support analytics and machine learning consumers, and collaborate with DevOps and platform teams on observability, security, and compliance. Mentor junior engineers and contribute to Agile delivery practices.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Migrate legacy data warehouse and ETL workloads, optimize Spark jobs and Delta tables, and implement data quality, governance, lineage, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate with platform, DevOps, and analytics teams on secure, observable data platforms.

Design, build, and support data services, pipelines and internal data architecture. Collaborate on product development, analyze and organize raw data, improve data quality, define best practices, and identify acquisition opportunities to support business insights and member-facing services.

Develop and maintain Tableau dashboards, workbooks, data sources, and interactive visualizations. Build and optimize SQL queries, data models, analytics-ready datasets, and ETL/ELT pipelines using Snowflake and enterprise data sources. Perform data profiling, validation, reconciliation, troubleshooting, testing, documentation, and performance optimization while translating business requirements into KPIs and reporting solutions.

Develop and maintain Python-based data and cloud migration frameworks for Snowflake and Azure. Migrate databases, stored procedures, analytic scripts, and potentially web applications to cloud platforms. Build scalable software solutions, participate in Agile delivery, testing, code reviews, technical design, requirements gathering, documentation, and technology evaluation. Collaborate with engineers, designers, managers, and financial-market users while maintaining quality, reporting progress, and owning projects through launch.

Leads the design and development of scalable on-premises and cloud-native data platforms. Builds production-grade ETL/ELT pipelines using big data technologies, supports governance and data quality, and contributes to architecture and planning. Mentors engineers, reviews code, solves complex data issues, and promotes maintainable, testable solutions. The role requires Python, Spark, Hadoop, SQL, Databricks, orchestration tools, and DevOps practices, with Java experience preferred.

Lead the design and implementation of scalable on-premises and cloud data platforms. Build and maintain ETL/ELT pipelines, support data governance and quality, participate in architecture and planning, and mentor engineers through code reviews and technical guidance. The role requires strong Python, Spark, Hadoop, SQL, Databricks, orchestration, DevOps, and CI/CD expertise, along with effective communication in English and Spanish.

Build and maintain large-scale ETL/ELT pipelines using Databricks, Spark, Python, Hadoop, and cloud data platforms. Develop and optimize PySpark data processing jobs, improve pipeline performance and cost, and support CI/CD, testing, deployment automation, and observability. Collaborate with Product Managers to define user-focused data modules and features. Debug complex data issues and communicate effectively with technical and non-technical stakeholders.

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 and maintain end-to-end data pipelines, models, data marts, and semantic layers supporting agency, client, BI, product, and AI consumers. Integrate fragmented marketing and customer data across advertising platforms, e-commerce, analytics, and CRM systems. Manage dbt projects, multi-tenant Snowflake infrastructure, data quality, performance, and client-specific modeling. Use AI coding agents, cloud infrastructure, CI/CD, and automated testing while collaborating across product, engineering, analytics, and client teams.

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.

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.

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.

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.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

Design, build, and optimize scalable data solutions enabling integration, processing, and availability of data for analytics, BI, and data science. Collaborate with multidisciplinary teams to implement ETL/ELT, data quality, testing, cloud deployments, APIs, and DevOps/DataOps practices across Azure and GCP.

As a Data Engineer, you will enhance the US Gas product by building data scrapers, improving APIs, and collaborating on features with the product team.

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.

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.