Sr Data Engineer (Databricks)

Location: MEXICO

Eligibility Notice This position is only open to candidates who are Mexican citizens currently residing in Mexico. Applications from candidates who do not meet this legal and operational requirement will not be considered. We appreciate your interest and encourage you to apply to roles that match your location.

Nexaminds is looking for a Senior Data Engineer to lead the development, optimization, and scaling of our data solutions. The ideal candidate has hands-on expertise building end-to-end pipelines on Databricks and enjoys working in a fast-paced, highly collaborative environment.

Qualifications we are looking for:

• 5+ years Data Engineering in production Azure environments — Python, SQL, Spark
• Python: production-grade OOP, config-driven design, no hardcoding, type annotations
• PySpark / Spark: DataFrames, schema enforcement, partitioning, performance tuning
• SQL: advanced window functions, CTEs, incremental load patterns, Delta Lake DML (MERGE, UPDATE, DELETE)
• Azure Data Factory: parameterised pipelines, linked services, triggers, IR configuration
• Azure Databricks: notebooks, Jobs API, DLT, cluster configuration, Unity Catalog access
• ADLS Gen2, Delta Lake / Parquet format, Medallion store patterns
• Testing discipline: pytest, unit and integration tests, data quality assertions
• Git: feature branching, PR workflow, commit discipline, code review

Nice to have:

• Scala: Spark Dataset API, typed transformations, sbt build tooling
• Healthcare data formats: EDI X12 (837/835/834), FHIR R4 resource parsing
• Delta Lake: schema evolution, time travel, OPTIMIZE, VACUUM, Z-ordering
• dbt (data build tool) for SQL transformation layering and lineage documentation
• Databricks Asset Bundles (DABs) for pipeline-as-code deployment
• DP-203 Azure Data Engineer Associate certification

Job duties:

• Design and build the core reusable ingestion engine in Python and ADF — parameterised, config-driven, zero hardcoding
• Build Python ingestion modules: file readers, schema validators, format handlers (CSV, EDI X12, FHIR R4, Parquet, JSON)
• Implement PySpark / Scala transformation components for batch and streaming at scale on Azure Databricks
• Write config-driven SQL data models for Bronze, Silver, G