Top Data Engineer Jobs in Tijuana

Build and govern transactional and credit behavior features for a credit scoring model using Databricks and Unity Catalog. Design reusable, auditable features with complete metadata, lineage, ownership, lifecycle, and temporal standards. Implement deployment pipelines, monitor jobs, apply data quality and FinOps practices, and maintain consistency between model training and production. Collaborate with modeling and platform teams while following advanced SQL, Python, and data governance practices.

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 for data products. Advise domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices. The role supports high-impact data engineering and machine learning projects for a multinational beverage company.

Own credit bureau furnishing operations, including data preparation, validation, submissions, reconciliation, and exception resolution. Build and debug SQL, Python, and data pipelines; investigate discrepancies; correct affected accounts; and implement testing, monitoring, and alerts. Partner with Customer Service, Compliance, and Engineering to resolve disputes and production defects. Document reporting logic, lineage, procedures, and incident resolutions while ensuring accurate, timely, and compliant credit reporting.

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.

Orion Innovation is seeking a Full Stack Engineer to support business transformation and product development through software engineering. The provided description contains no specific responsibilities, technologies, qualifications, location, travel requirements, or compensation details.

Architects and builds secure, scalable data platforms and governance automation. Develops metadata, lineage, data quality, access control, policy enforcement, APIs, MCP tools, and enterprise integrations. Implements identity, authorization, credential management, testing, CI/CD, monitoring, observability, and production support across environments. Partners with governance and engineering teams, documents solutions, and mentors engineers while guiding technical direction.

Develops data integration and analytics solutions for financial loss reporting. Partners with stakeholders, product owners, SMEs, and technology teams to document requirements, validate business rules, support source-to-target mapping and UAT, certify data products, resolve data quality issues, and improve dashboards, reports, data platforms, and downstream data consumption.

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.

Owns credit bureau furnishing operations, including data preparation, validation, submissions, reconciliation, and exception resolution. Builds and debugs SQL, Python, and data pipeline workflows; investigates reporting discrepancies; implements corrections and controls; and develops testing, monitoring, and alerting. Partners with Customer Service, Compliance, and Engineering to resolve disputes, translate reporting requirements, fix production issues, and document data lineage and procedures.

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.

Designs and deploys secure AWS cloud architectures, leads enterprise migrations and application modernization, and builds Generative AI and machine learning solutions using Bedrock, SageMaker, vector databases, and RAG. Acts as the primary technical leader for client engagements, advises executive stakeholders, develops proofs of concept, drives AWS adoption and account growth, and mentors engineering teams while creating reusable infrastructure and delivery frameworks.

The Staff Engineer, Data Science role focuses on leveraging tech skills to enhance team capabilities and provide advanced solutions for client challenges.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

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.

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.