Expert Data Engineer
Hybrid Ciudad de Mexico Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Nestlé. Ciudad de Mexico.
Pay not stated Data Engineering salary
RoleData Engineering
SeniorityMid-level
LocationCiudad de Mexico
WorkplaceHybrid
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build and improve shared data platform capabilities for ingesting, transforming, storing, governing and serving data
- Create reusable pipelines, frameworks and automation that reduce duplicated work and operational friction
- Implement batch, streaming and near-real-time processing, then tune platform components for speed, resilience and cost
- Bake security, quality, lineage and access controls into data platform patterns and services
- Maintain deployment, testing and monitoring practices, and resolve complex pipeline or platform failures
What you bring
- At least eight years in data engineering, cloud data platforms, or a closely related technology role
- A relevant bachelor's degree, or equivalent professional experience
- Strong SQL and proficiency in Python, Scala or Java, with sound practices for reusable and testable code
- Hands-on experience building distributed data systems and operating batch, streaming and near-real-time data flows
- Fluent business English and experience with CI/CD, automation, monitoring and incident investigation
Who this fits
This role suits a senior hands-on engineer who can contribute as a technical expert and work across business, platform and engineering teams. The job is hybrid in Ciudad de Mexico, and business English fluency is required.
From the employer
Expert Data Engineer
Position Snapshot:
Type of Contract: Full Time
Function and Stream: NUSA
Type of work: Hybrid
Work Language: Fluent Business English
The role:
The Expert Data Engineer will be responsible for building, maintaining, and optimizing core data platform capabilities that enable scalable data ingestion, processing, storage, and access across tools such as Databricks, Azure, and Snowflake. This role develops reusable pipelines, frameworks, automation, and platform patterns that ensure data is reliable, performant, secure, cost-effective, and aligned with enterprise standards for governance, security, quality, and interoperability.
The role will operate as an expert technical contributor, partnering closely with business, analytics, data product, engineering, architecture, security, platform, and regional/global IT stakeholders to strengthen the data foundation for BI, advanced analytics, AI/ML, and enterprise data products.
What you’ll do:
As an Expert Data Engineer you will:
- Design, build, maintain, and optimize core enterprise data platform capabilities that support scalable data ingestion, processing, storage, transformation, governance, and access across Azure Datalake, Databricks, Snowflake, and related cloud data services.
- Develop reusable data pipelines, engineering frameworks, platform services, templates, automation scripts, and standard patterns that accelerate delivery while reducing operational friction, duplication, and technical risk.
- Architect and implement high-throughput batch, streaming, and near-real-time data processing capabilities using modern data engineering patterns, distributed processing frameworks, orchestration tools, and cloud-native services.
- Optimize Databricks, Snowflake, Azure Synapse, and related platform components for performance, reliability, scalability, cost efficiency, observability, and operational resilience.
- Embed governance, security, quality, lineage, metadata, access controls, auditability, and compliance requirements into platform capabilities and data engineering patterns by design.
- Build and maintain CI/CD, Infrastructure-as-Code, automated testing, monitoring, alerting, deployment, and environment management practices for data platform components and configurations.
- Partner with data engineers, analytics engineers, data product teams, architects, platform teams, and business stakeholders to translate data needs into scalable, reusable, and supportable platform capabilities.
- Troubleshoot complex data pipeline, platform, integration, access, compute, storage, performance, and reliability issues; lead root-cause analysis and drive durable remediation.
- Create documentation, reference implementations, runbooks, standards, and enablement materials that improve developer productivity, self-service adoption, and consistent use of platform capabilities.
- Stay current with modern data platform engineering practices, including lakehouse architectures, distributed systems, data observability, data contracts, metadata-driven automation, FinOps, MLOps enablement, and platform-as-product operating models.
We offer you: We offer more than just a job. We put people first and inspire you to become the best version of yourself.
- Great benefits including salary and a comprehensive social benefits package. We have one of the best pension plans on the market, as well as flexible remuneration with tax advantages: health insurance, restaurant card, mobility plan, etc.
- Personal and professional growth through ongoing training and constant career opportunities reflecting our conviction that people are our most important asset.
- Hybrid working environment with flexible working scheme. Our state-of-the-art campus is dog friendly and equipped with a medical center, canteen and areas to co-create network and chill!
- Recreation activities such as yoga, Zumba, etc. and a wide range of volunteering activities.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Management, Data Analytics, or a related field; equivalent experience will be considered.
- 8+ years of experience in data engineering, data platform engineering, cloud data platforms, software engineering for data systems, or related technology roles.
- Expertise building and optimizing scalable distributed data systems across platforms such as Databricks, Microsoft Azure, Spark, SQL-based platforms, cloud storage, and cloud-native data services.
- Advanced proficiency with SQL and a modern programming language such as Python, Scala, or Java, with strong software engineering practices for reusable, testable, and maintainable code.
- Deep experience designing and operating data lakes, lakehouses, data warehouses, hybrid architectures, data pipelines, orchestration frameworks, and enterprise-scale ingestion and transformation patterns.
- Strong hands-on experience with batch, streaming, and near-real-time data processing; ETL/ELT frameworks; structured, semi-structured, and unstructured data integration; and high-volume data movement patterns.
- Strong knowledge of CI/CD, Infrastructure-as-Code, automation, monitoring, alerting, observability, incident response, root-cause analysis, environment management, and operational excellence practices.
- Strong communication, problem-solving, documentation, stakeholder management, and cross-functional collaboration skills, with fluency in English.
- Proactive, agile, and self-sufficient mindset, with the ability to work effectively as an expert technical contributor in a dynamic environment.
- Experience with Snowflake capabilities such as warehouse optimization, secure data sharing, role-based access, performance tuning, storage and compute management, and governed data consumption patterns.
- Experience with medallion/lakehouse architectures, data contracts, schema evolution, change data capture, event-driven pipelines, data observability, and reliability engineering for enterprise data systems.
- Experience with orchestration and transformation tools such as Airflow, dbt, Azure Data Factory, Databricks Workflows, Informatica, or equivalent enterprise data integration tools.
- Experience with Terraform or similar Infrastructure-as-Code tools, Git-based development, automated testing, deployment automation, secrets management, and platform configuration management.
- Exposure to AI/ML enablement, feature engineering, feature stores, vector search, MLOps patterns, and data platform capabilities that support advanced analytics and GenAI use cases.
- Certifications such as Azure Data Engineer, Databricks Data Engineer, Snowflake SnowPro, Microsoft Fabric Analytics Engineer, or related cloud/data platform credentials are a plus.