Top Data Engineer Jobs in Tijuana

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.

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 operate a production Snowflake-based data platform on Azure: provision infrastructure with Terraform, build Bronze/Silver/Gold models with SQLMesh, develop ingestion pipelines (ADF, Event Hub, Functions), implement CI/CD (GitHub Actions), enforce governance and HIPAA-aligned controls, implement observability, write production Python, and support stakeholders in a solo senior contributor role.

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.

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.

Design, build, and maintain ETL pipelines and orchestration on AWS using Python/PySpark. Integrate APIs and event-driven systems, optimize storage/querying (Redshift/RDS/Oracle/S3), implement data quality and monitoring, support CI/CD deployments, and collaborate with stakeholders to deliver data solutions.

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

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

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.