Role Description 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. Location: MEXICO, CANADA Qualifications - 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 Requirements - 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, Gold medallion transformations - Develop metadata-driven validation layer: null checks, type enforcement, range rules, referential integrity - Build reusable utility libraries: logging, error handling, retry logic, dead-letter routing - Implement Databricks notebooks and DLT (Delta Live Tables) pipelines for declarative transformations - Build and maintain the onboarding template library v1 and v2 — parameterised, documented, production-ready - Onboard Provider, Claims, Member, Eligibility, and Reference data domains using the framework - Write unit tests, integration tests, and data contract tests (pytest, Great Expectations or equivalent) - Optimise Spark jobs: partitioning, caching, broadcast joins, Z-ordering on Delta tables - Participate in code review, follow GitHub branching standards, and contribute to documentation Benefits - Stock options 📈 - Remote work options 🏠 - Flexible working hours 🕜 - Benefits above the law But it's not just about the work; it's about the people too. You'll be collaborating with some seriously awesome IT pros. You'll have access to mentorship and tons of opportunities to learn and level up. Ready to embark on this journey with us? 🚀🎉 If you're feeling the excitement, go ahead and apply!