Top Data Engineer Jobs in Tijuana

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 and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design, develop, and maintain a scalable open-source data platform centered on lakehouse architecture, ETL/ELT pipelines, workflow orchestration, and analytical databases. Build and monitor batch data-processing systems, optimize infrastructure performance and reliability, and implement data quality, governance, and security practices. Collaborate with data scientists, analysts, and backend engineers to deliver reliable data solutions across cloud and microservices environments.

Designs and maintains scalable Azure-based data pipelines and infrastructure for batch, real-time, analytics, and reporting workloads. Builds Databricks Spark applications, Lakehouse architectures, Delta Tables, APIs, and streaming ingestion frameworks. Responsibilities include data governance, security, compliance, monitoring, troubleshooting, performance optimization, DataOps, and CI/CD automation. Collaborates with data scientists, analysts, application teams, and business stakeholders to deliver reliable data solutions.

Designs, builds, deploys, and optimizes cloud-based data pipelines on GCP. Integrates data from databases, APIs, and streaming platforms using ETL and ELT processes; manages BigQuery and NoSQL databases; performs data modeling and query optimization; and uses Airflow and modern transformation tools. Collaborates with data scientists, analysts, clients, and business stakeholders in distributed multinational teams.

Designs and maintains scalable cloud-native ETL/ELT pipelines, data models, Lakehouse architectures, APIs, and real-time streaming solutions. Uses Azure, Databricks, Spark, Python, SQL, and distributed data technologies to support analytics and business intelligence. Ensures data quality, governance, security, performance, and reliability while collaborating with analysts, data scientists, application teams, and stakeholders. Supports DataOps, CI/CD, monitoring, troubleshooting, and automated deployment of enterprise data platforms.

Leads the design and modernization of enterprise data platforms, pipelines, and lakehouse architectures supporting credit risk, decisioning, analytics, and AI initiatives. Architects scalable ETL/ELT and streaming solutions, establishes data quality, governance, lineage, security, and observability standards, and optimizes distributed workloads. Partners with cross-functional stakeholders, drives technical roadmaps and architectural decisions, mentors engineers, supports compliance and audits, and leads complex initiatives across multiple teams.

Build and maintain scalable batch and streaming ETL/ELT pipelines using Python, SQL, PySpark, Databricks, and Delta Lake. Design medallion data models, ingest diverse sources, optimize Spark and SQL performance, implement data quality monitoring, orchestrate workflows, and apply testing, version control, and CI/CD practices. Partner with analysts, data scientists, and stakeholders to create reliable datasets, while documenting lineage and design decisions.

Migrate insurance policy data from legacy administration systems to a modern platform. Analyze, trace, reconcile, map, transform, cleanse, and validate data across databases and files. Build SQL queries and ETL processes, resolve data quality issues, and support production data loads. Collaborate with technical and non-technical stakeholders throughout the migration lifecycle while using AI coding tools, Agile methods, and secure data-handling practices.

Lead the development, optimization, and scaling of production-grade data ingestion and transformation pipelines. Design bulk onboarding, backfills, migrations, and event-driven data processes with monitoring, idempotency, rollback plans, data quality controls, reconciliation, anomaly detection, and auditability. Collaborate with Platform and SRE teams to improve production safety and observability, while producing runbooks, cutover checklists, validation reports, and post-mortem follow-ups.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Designs and operates production data platforms supporting warehouses, streaming and batch pipelines, semantic layers, RAG systems, embeddings, vector stores, and AI feature pipelines. Responsibilities include dimensional modeling, data contracts, lineage, observability, testing, governance, cost optimization, CI/CD, cloud infrastructure, and API delivery. Partners with data scientists and stakeholders, mentors engineers, and ensures reliable, secure, and maintainable data systems.

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.

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 operate scalable Azure-based data, analytics, automation, and tooling solutions for Microsoft's Finance Data & Experiences organization. Responsibilities include software development, infrastructure as code, Azure DevOps pipelines, observability, incident response, cost optimization, security, and production reliability. The role involves managing cloud infrastructure, automating operations, integrating AI capabilities, and collaborating with cross-functional engineering and business 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.