Top Data Engineer Jobs in Tijuana

Design, develop, deploy, and operate scalable cloud-based data, analytics, automation, and tooling solutions. Integrate Azure AI and machine learning capabilities, implement monitoring and telemetry, analyze usage data, and collaborate with cross-functional teams to deliver customer-focused systems. Own solutions across design, implementation, and operations while contributing to Microsoft’s data platform innovation.

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.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Designs and maintains automated AWS data pipelines using Python, Airflow, dbt, SQL, and cloud storage. Responsibilities include pipeline orchestration, error troubleshooting, data imports and exports, Snowflake query optimization, reusable code generation, testing, security, and GitHub-based CI/CD. The role also supports AWS services, API integrations, monitoring, data quality validation, and collaboration with global teams in English and Spanish.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon DSP and other data sources, design schemas and transformations, evaluate managed ELT solutions, and ensure outputs support attribution, scoring, dashboards, and predictive models. Provide broader application and backend engineering support within the existing AWS environment while collaborating with infrastructure and data science teams.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs; guide domain teams on data modeling and product boundaries; mentor engineers and analysts; and improve reusable platform templates, tooling, and engineering practices.

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, operate, and scale change data capture pipelines using Debezium, Kafka or Azure Event Hubs, and Databricks. Manage streaming data across hundreds of databases and thousands of tables, ensuring reliability, low latency, schema consistency, and data quality. Troubleshoot replication lag, schema drift, backpressure, and recovery issues. Collaborate with backend and data teams on schemas and SLAs while documenting architecture and operational procedures.

Designs, builds, and operates large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark and streaming workloads; managing distributed systems, databases, and workflow orchestration; troubleshooting production platforms; and delivering reliable, performant, cost-effective data infrastructure for analytics, machine learning, and reporting.

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 operate the people-data platform supporting analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, governance, privacy, and access controls. Monitor reliability, manage incidents, document technical assets, and define infrastructure improvements. Own the people data lake’s AI-readiness roadmap and collaborate with the People Analytics Product Owner, BI Developer, enterprise data, HR technology, security, legal, and privacy teams.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Build and enhance data engineering frameworks, scalable data solutions, centralized ETL logic, metric definitions, and concise data models. Develop self-service data tools supporting data science, internal applications, and organizational decision-making. Collaborate with cross-functional stakeholders, use AI coding agents such as Claude Code or Codex, and write production-level Python and SQL.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

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.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.

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.

Lead the architecture and hands-on development of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, pipelines, orchestration, governance, observability, DataOps, CI/CD, performance, cost optimization, MLOps, and AI-readiness initiatives. Mentor analytical engineers, evaluate modern tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

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.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

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.

Build and maintain scalable data pipelines, ETL/ELT processes, data models, metric definitions, and data infrastructure. Develop production solutions with Python, SQL, Spark, dbt, Fivetran, Airflow, Databricks, Azure, and cloud storage. Collaborate with data scientists, engineers, analysts, and stakeholders to deliver reliable, accessible data and self-service tools. Troubleshoot pipeline issues, improve data quality and scalability, and use AI-assisted development tools to accelerate engineering work.

Designs and operates scalable data architectures, pipelines, ETL processes, databases, warehouses, and lakes. Improves data quality, governance, observability, security, and reliability while partnering with stakeholders to develop effective data solutions. Provides technical leadership, mentors engineers, leads code reviews and cross-functional initiatives, resolves production issues, and drives improvements to engineering platforms and practices.