Top Data Engineer Jobs in Leon

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.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Designs and maintains scalable batch and real-time ETL/ELT pipelines, data models, Lakehouse architectures, APIs, and streaming frameworks. Builds Azure- and Databricks-based data solutions using Spark, Delta Lake, and related services. Ensures data quality, governance, security, compliance, and platform reliability while supporting DataOps, CI/CD, monitoring, and performance optimization. Collaborates with data scientists, analysts, application teams, and stakeholders on enterprise data solutions.

Design, build, and optimize large-scale data pipelines using Databricks, PySpark, ETL/ELT, and lakehouse architectures. Ensure data quality, reliability, performance, and observability while applying Spark optimization techniques. Collaborate with architects, analysts, and stakeholders to deliver scalable data solutions. Implement CI/CD, version control, workflow orchestration, and automation using tools such as Azure Data Factory, Airflow, or Fabric. Communicate technical solutions effectively across technical and non-technical teams.

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.

Develops data-driven solutions for finance, accounting, and regulatory reporting. Automates reconciliations, builds self-service data tools, creates dashboards and metrics, analyzes business strategies, and monitors data quality. Partners with finance, business, and technology teams to translate requirements into technical solutions while supporting data governance, security, access controls, metadata, lineage, and compliance.

Build and operate modern lakehouse platforms using Microsoft Fabric and OneLake. Design ingestion pipelines, notebooks, transformations, curated data layers, quality checks, monitoring, alerts, and documentation. Troubleshoot pipeline and performance issues across ingestion and consumption layers, ensure reliable data products, and collaborate with architects, analytics, and BI teams to support reporting and adoption.

Designs and develops scalable data pipelines, ETL/ELT processes, and integrations that synchronize enterprise data into Algolia indices. Optimizes indexing performance, data quality, and search relevance while implementing monitoring, governance, and validation. Collaborates with application, product, and business teams to define search requirements, troubleshoots synchronization and performance issues, and supports integrations with CMS, eCommerce, enterprise, and customer-facing platforms.

Designs and maintains scalable data pipelines, data models, semantic layers, data marts, and Tableau reporting solutions. Partners with stakeholders to translate reporting needs into technical solutions, while ensuring data quality, governance, performance, and reliability. Optimizes SQL queries, ETL/ELT processes, data warehousing, and Tableau performance across large-scale cloud data platforms. Collaborates with architects, analysts, and engineering teams on enterprise data solutions.

Leads end-to-end data migration projects, including profiling, quality assessment, source-to-target mapping, transformation logic, cleansing, validation, reconciliation, and UAT support. Collaborates with stakeholders to define requirements and acceptance criteria, recommends migration strategies, monitors risks, maintains governance and security standards, and produces documentation, reports, and quality metrics.

Designs and implements scalable data integration, migration, ETL/ELT, and data modeling solutions. Optimizes Elasticsearch indices and clusters, develops SQL queries and database objects, and builds integration services using Java, TypeScript, or C#. Supports data validation, reconciliation, CI/CD, testing, deployment, troubleshooting, architecture reviews, and enterprise data platform operations while collaborating with technical and business stakeholders.

Designs and maintains Coveo data ingestion and indexing pipelines, integrating enterprise data sources and configuring metadata, schemas, mappings, and enrichment. Optimizes search relevance using query pipelines, ranking strategies, and machine learning. Monitors analytics and platform performance, troubleshoots synchronization issues, supports data governance, and contributes to AI Search, Generative AI, and knowledge management initiatives. Partners with engineering, product, and business teams to improve enterprise information access.

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.

Designs, builds, deploys, and operationalizes complex data engineering solutions for multiple customers. Responsibilities include Databricks migrations, data pipeline development, transformations, optimization, governance, security, quality, testing, incident resolution, and data source analysis. The role also involves advising customers, tracking project progress, collaborating across teams, and educating users on data products and analytics environments.

Designs, develops, and maintains scalable batch and real-time data pipelines and curated datasets for enterprise credit risk, lending, and analytics. Builds solutions with Databricks and cloud-native platforms, implements data quality, monitoring, testing, and observability, and supports platform modernization. Partners with technical and business teams, troubleshoots production issues, contributes to architecture, improves reliability and efficiency, and mentors junior engineers.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

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.

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.