Senior Databricks Engineer ID86295

AgileEngine, LLC.; Rosarito; Presencial; MXN 2.063.000 - 3.095.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Descripción de la vacante

AgileEngine in Mexico is seeking a Senior Data Engineer to build batch and streaming pipelines on Databricks using PySpark and Delta Lake. You will migrate legacy data workloads to a governed Lakehouse with Unity Catalog governance and model medallion architecture for analytics and ML.

Strong SQL and Python, experience with streaming ingestion, and collaboration with DevOps and analytics teams are essential. Competitive USD compensation and flexible work options are offered.

Formación

• 3+ years of professional data engineering experience.
• Hands-on with Apache Spark, PySpark, and Delta Lake.
• Strong SQL and Python skills with data modeling.
• Experience migrating ETL workloads to Lakehouse.
• Familiarity with Unity Catalog and data governance.
• Experience with streaming ingestion (Structured Streaming, Kafka, or Event Hubs).
• Experience with Databricks Workflows, Airflow, or Azure Data Factory.
• Secure coding practices and compliance awareness.

Responsabilidades

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake.
• Model a medallion architecture for analytics and ML.
• Migrate legacy ETL workloads to Lakehouse with data parity.
• Use CI tools to accelerate development, tests, and docs.
• Write clean Python and SQL with code reviews.
• Optimize Spark jobs and Delta tables for performance and cost.
• Implement data quality, lineage, and governance using Unity Catalog.
• Debug pipeline issues and production incidents.
• Collaborate with DevOps and analytics engineers on observability and security.

Conocimientos

Databricks

PySpark

Delta Lake

SQL

Python

Lakehouse

Data Modeling

Structured Streaming

Kafka

Databricks Workflows

Unity Catalog

Governance

dbt

Herramientas

Airflow

Azure Data Factory

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 Senior Data Engineer to build batch and streaming pipelines on Databricks using PySpark and Delta Lake. This person migrates legacy data warehouse and ETL workloads onto a governed Lakehouse, modeling a medallion architecture that powers analytics and AI use cases. Strong SQL, Python, and experience with Unity Catalog governance round out the role.

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.

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

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.