Key Responsibilities

Data Engineering & Platform Development

Design, develop, and maintain scalable batch and streaming data pipelines using Databricks and Apache Spark.

Build and optimize ingestion frameworks to ingest data from ERP, MES, IoT, CRM, supply chain, and external partner systems.

Develop robust ELT/ETL pipelines leveraging Delta Lake, Spark SQL, and PySpark.

Implement framework-driven engineering practices to improve reusability, scalability, and maintainability.

Enterprise Data Modeling

Design and implement enterprise data models supporting reporting, analytics, data science, and AI use cases.

Build and maintain Bronze, Silver, and Gold layers following Medallion Architecture principles.

Create dimensional models, semantic layers, and curated business-ready datasets.

Partner with business stakeholders to translate requirements into scalable data structures.

Data Foundation Transformation

Contribute to the redesign of enterprise-wide data architecture and governance standards.

Help establish data quality, lineage, metadata management, and monitoring frameworks.

Drive modernization efforts by migrating legacy pipelines and data warehouses onto Databricks.

Performance & Reliability

Optimize Spark jobs and data models for performance, scalability, and cost efficiency.

Implement automated testing, CI/CD deployment processes, and observability solutions.

Ensure data reliability through validation, reconciliation, and monitoring controls.

Cross-functional Collaboration

Work closely with Product Managers, Data Architects, Analytics teams, Manufacturing SMEs, and Business stakeholders.

Support analytics, reporting, machine learning, and operational decision-making use cases.

Mentor junior engineers and promote engineering best practices across the team.

Required Qualifications

Education





Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.

Experience

5-8 years of experience in Data Engineering, Data Warehousing, or Big Data development.

3 years of hands-on experience with Databricks and Apache Spark.

Experience building enterprise-scale data platforms in cloud environments.

Technical Skills

Databricks Ecosystem

Strong hands-on experience with:

Databricks Lakehouse Platform

Delta Lake

Unity Catalog

Databricks Workflows

Structured Streaming

Delta Live Tables (preferred)

Programming & Data Processing

Advanced proficiency in

PySpark

Spark SQL

Python

SQL

Cloud Platforms

Experience with one or more:

Azure (preferred)

AWS

GCP

Data Modeling

Strong understanding of

Dimensional Modeling

Star/Snowflake Schemas

Data Vault (preferred)

Lakehouse Architecture

Medallion Data Design Patterns

DevOps & Engineering Practices

Git-based source control

CI/CD pipelines

Infrastructure as Code concepts

Data Quality and Monitoring frameworks

Preferred Industry Experience

Consumer Packaged Goods (CPG)

Manufacturing

Food & Beverage

Bottled Water / Beverage Production

Supply Chain & Logistics Analytics

Experience working with data from:

SAP ECC/S4HANA

Manufacturing Execution Systems (MES)

Warehouse Management Systems (WMS)

Transportation Management Systems (TMS)

IoT and production telemetry sources is highly desirable.

Desired Competencies

Strong analytical and problem-solving skills.

Ability to navigate ambiguity in large-scale transformation programs.

Excellent stakeholder management and communication skills.

Passion for building scalable enterprise data platforms.

Ownership mindset with focus on quality and operational excellence.

Ability to influence architecture and engineering decisions.

Mandatory: Databricks, data modelling,

📌 Senior Data Engineer (México)
🏢 Entellux
📍 México