Top Data Engineer Jobs in Tijuana

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.

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.

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.

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.

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

Designs, develops, tests, and supports scalable data pipelines, data products, and data services. Builds batch and event-driven ingestion, transformation, publishing, and integration solutions using SQL and Python. Develops APIs, supports cloud services and streaming platforms, improves data quality and reliability, and contributes to canonical data models, master data management, data standardization, and enterprise data architecture decisions.

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.

Build and maintain data models, dashboards, reports, and dbt models supporting cross-functional decision-making. Process large datasets, establish reporting sources of truth, bridge technical and business teams, and document knowledge bases and business definitions for internal AI systems. The role also involves identifying cost-saving opportunities, communicating with stakeholders, and using AI to develop solutions and fix bugs.

Build and deliver scalable AWS data platforms, ETL pipelines, lakehouse solutions, and migration projects for customers. Develop Databricks environments using Spark, Delta Lake, and job orchestration; troubleshoot performance, scalability, and reliability issues; contribute to solution design, automation, security, cost optimization, documentation, and implementation reviews. Collaborate with cross-functional teams and customers through agile ceremonies while communicating technical concepts clearly.

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.

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.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Lead design and delivery of end-to-end healthcare data platforms using Snowflake, dbt, and Azure services. Model EHR/claims/lab data, build and optimize pipelines (Python/SQL), ensure regulatory/privacy/security compliance, apply data quality frameworks, run stakeholder workshops, and leverage AI tools to accelerate development and governance.

Build and maintain data pipelines, dbt transformations, SQL models, and analytics datasets. Develop Power BI dashboards, semantic models, and DAX measures while validating data quality and troubleshooting issues end to end. Translate business needs into reporting solutions, support existing analytics environments, and independently deliver production-ready work. Collaborate with business, analytics, and engineering teams while applying AI tools to improve productivity.

Build and maintain scalable pipelines for conversational and unstructured text data. Responsibilities include ingestion, transformation, enrichment, sampling, validation, monitoring, quality control, and preparation of datasets for analytics, prompt training, machine learning, and AI applications. The role collaborates with distributed Text Analytics, Data Science, Engineering, and Product teams while documenting pipeline logic and ensuring performance, reliability, scalability, and maintainability.

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.

Senior UI developer responsible for designing and building responsive React-based UIs and dashboards for cybersecurity analytics. Develop backend services and APIs in Python, integrate security data sources, optimize SQL Server/PostgreSQL reporting, collaborate with stakeholders, influence architecture, and mentor junior developers.

Design and develop enterprise Test Data Management solutions: self-service data provisioning, masking, subsetting, synthetic data generation, SQL/API validations, Gold Copy databases, Mainframe JCL extracts to MFT, and collaborate with QA/Dev/DevOps to automate test data workflows.

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.

Lead evaluation and benchmarking of enterprise-scale graph databases. Extract, transform, and load data from Databricks into multiple graph platforms, implement benchmark workloads and APIs, analyze query plans and indexing, provision test environments, document results, and advise architects and leaders on technical trade-offs and recommendations.