Middle Databricks Data Engineer ID86295

AgileEngine; Ciudad de México; Presencial; MXN 600.000 - 850.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Growth opportunities

Competitive pay

Remote work

Modern projects

Collaborative culture

Well-being programs

Descripción de la vacante

AgileEngine is seeking a Middle Data Engineer to modernize a data warehouse to a Databricks Lakehouse. You will design batch and streaming pipelines with PySpark and Delta Lake across bronze, silver, and gold layers, leveraging AI tools to accelerate development.

The role emphasizes data governance, production-grade pipelines, and collaboration with DevOps and analytics teams. Remote work options are available.

Formación

• 3+ years of data engineering with Spark and cloud architectures.
• Strong hands-on with Databricks, Spark (PySpark) and Delta Lake.
• Advanced SQL and Python with Lakehouse data modeling.
• Streaming ingestion using Structured Streaming, Auto Loader, Kafka or Event Hubs.
• Workflow orchestration with Databricks Workflows, Airflow or Azure Data Factory.
• Experience migrating legacy ETL and modernizing data warehouses.
• Strong problem-solving, collaboration, and communication.
• Familiarity with Unity Catalog, data governance and PII handling.
• Experience with dbt or comparable transformation framework.
• Secure coding practices and security standards.
• Experience delivering production data platforms at scale.
• Upper-intermediate English level.

Responsabilidades

• Design, build, and operate batch and streaming pipelines on Databricks.
• Model and maintain a medallion architecture for analytics and ML.
• Migrate workloads to Lakehouse with data parity and minimal disruption.
• Utilize Claude or Copilot to accelerate development and testing.
• Write clean Python and SQL with thorough code reviews.
• Optimize Spark jobs and Delta tables for performance and cost.
• Implement data quality, lineage, and governance using Unity Catalog.
• Debug and resolve pipeline failures and data defects.
• Collaborate with DevOps, platform, and analytics teams on observability and security.

Conocimientos

Databricks

PySpark

Spark

Delta Lake

SQL

Python

Structured Streaming

Kafka

Event Hubs

Airflow

Databricks Workflows

Azure Data Factory

Unity Catalog

dbt

Security practices

English Proficiency

Production data platforms

Herramientas

Terraform

CloudWatch

PostgreSQL

Descripción del empleo

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you’re looking for a place to grow, make an impact, and work with people who care, we’d love to meet you!

ABOUT THE ROLE

We are looking for a Middle Data Engineer to help modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will build batch and streaming pipelines with PySpark and Delta Lake, following a medallion architecture across bronze, silver, and gold layers. This role also uses AI tools like Claude and Github Copilot to speed up development.

WHAT YOU WILL DO

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or Github Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

MUST HAVES

• 3+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.
• Experience delivering production data platforms at scale.
• Upper-intermediate English level.

NICE TO HAVES

• Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure DevOps.
• Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.
• Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).
• Experience working in Agile or team-based development environments preferred.

PERKS AND BENEFITS

• Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
• Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
• Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
• Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
• Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
• Well-being & support: access local well-being programs and people-focused support tailored to your location