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Senior Data Engineer (Databricks / Spark Streaming)
Senior Data Engineer (Databricks / Spark Streaming)
Location: Remote (Mexico)
About the Role
We are looking for a Senior Data Engineer to design, build, and scale our data infrastructure with a focus on real-time and batch processing pipelines. You will work extensively with Databricks and Apache Spark Structured Streaming to deliver reliable, high-throughput data platforms that power analytics, machine learning, and business-critical applications. This is a fully remote position open to candidates based in Mexico.What You'll Do
- Design, build, and maintain scalable ETL/ELT pipelines using Databricks and Apache Spark
- Develop and optimize real-time streaming pipelines using Spark Structured Streaming (Kafka, Kinesis, Event Hubs, or similar)
- Architect and manage Delta Lake tables, implementing best practices for schema evolution, partitioning, and performance tuning
- Build and maintain data pipelines across the medallion architecture (bronze/silver/gold layers)
- Collaborate with data scientists, analysts, and software engineers to understand data needs and deliver production-grade solutions
- Implement CI/CD workflows for data pipelines using tools such as Databricks Asset Bundles, GitHub Actions, or Azure DevOps
- Monitor, troubleshoot, and optimize pipeline performance, cost, and reliability
- Establish and enforce data quality, governance, and observability standards (e.g., using Unity Catalog, Great Expectations, or similar)
- Mentor junior engineers and contribute to engineering best practices and documentation
- Participate in architecture discussions and provide technical guidance on data platform strategy
- 8+ years of experience in data engineering, with a strong focus on distributed data processing
- Hands-on production experience with Databricks (clusters, jobs, workflows, Unity Catalog)
- Strong expertise in Apache Spark , including Spark Structured Streaming for real-time data processing
- Proficiency in Python and/or Scala for data pipeline development
- Advanced SQL skills for data transformation and optimization
- Experience with Delta Lake and lakehouse architecture concepts
- Experience with AWS — Databricks-supported environments
- Familiarity with message streaming systems such as Kafka , Kinesis , or Event Hubs
- Experience with orchestration tools (e.g., Databricks Workflows, Airflow)
- Solid understanding of data modeling, partitioning strategies, and performance optimization
- Experience with version control (Git) and CI/CD practices for data engineering
- Databricks certifications (e.g., Databricks Certified Data Engineer Professional)
- Experience with infrastructure-as-code (Terraform)
- Experience with dbt for transformation workflows
- Familiarity with MLOps or feature store concepts
- Experience working in a fully remote, distributed team environment
- Prior experience in a regulated industry (finance, healthcare, etc.)
- Strong communication skills and comfort working async with distributed, cross-functional teams
- A proactive, ownership-driven mindset — comfortable taking projects from design through production
- Ability to balance pragmatic delivery with long-term architectural quality
- Fluent English and Spanish (written and verbal)