Top Data Engineer Jobs in Tijuana

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 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.

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.

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.

Develop and maintain Power BI dashboards, reports, semantic models, and data solutions using enterprise sources including Snowflake and SAP HANA. Partner with stakeholders to define KPIs, requirements, and data models; support data onboarding and integration; troubleshoot issues across source systems, data platforms, and reporting layers; improve data quality, governance, consistency, and reusable analytics solutions.

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.

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.

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.

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 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.

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.

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.

Leads the design, development, deployment, and maintenance of data pipelines and analytics platforms using Microsoft Fabric and Azure. Builds Lakehouse and Warehouse solutions, develops transformations with SQL, Python, and PySpark, and ensures data quality, security, performance, and governance. Collaborates with architects, developers, analysts, and stakeholders in Agile teams, contributes to CI/CD and documentation, leads technical discussions, mentors junior engineers, and provides technical guidance across client engagements.

Designs, builds, and maintains large-scale data pipelines using Databricks, PySpark, and Apache Spark. Develops ETL/ELT processes, lakehouse architectures, and optimized data workflows while ensuring quality, reliability, performance, and observability. Collaborates with architects, analysts, and stakeholders to deliver scalable data solutions. Uses orchestration, version control, CI/CD, and automation practices to support modern analytics platforms.

Designs, builds, and maintains scalable data pipelines, data warehouse architecture, feature stores, model-training workflows, and real-time inference services. Develops semantic search, vector database, RAG, LLM orchestration, dashboarding, and AI-powered BI solutions. Partners with stakeholders, documents workflows, supports model deployment, and applies data engineering and MLOps best practices using Python, Spark, Hadoop, Kafka, SQL, and cloud databases.

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.

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.