Data Solutions Engineer (Scala / Spark)

Role Summary :

Design, build, and optimize large-scale ETL and data-processing pipelines handling GB–TB volumes. Operate within the Databricks ecosystem and drive migration of selected workloads to high-performance engines such as Polars and DuckDB. Maintain strong engineering rigor across CI / CD, testing, and code-quality enforcement. Apply analytical thinking to solve data reliability, performance, and scalability problems. AI familiarity is advantageous.

Core Responsibilities :

• Develop and maintain distributed data pipelines using Scala, Spark, Delta, and Databricks.
• Engineer robust ETL workflows tuned for high-volume ingestion, transformation, and publishing.
• Profile pipelines, remove bottlenecks, and optimize compute, storage, and job orchestration.
• Lead migration of suitable workloads to Polars, DuckDB, or equivalent high-performance engines.
• Implement CI / CD workflows with automated builds, tests, deployments, and environment gating.
• Enforce coding standards through code coverage targets, unit / integration tests, and SonarQube rules.
• Ensure pipeline observability : logging, data quality checks, lineage, and failure diagnostics.
• Apply analytical reasoning to triage complex data issues and deliver root-cause clarity.
• Contribute to AI-aligned initiatives when required : RAG design, fine-tuning workflows, agentic patterns.
• Collaborate with product, analytics, and platform teams to operationalize data solutions

Required Skills and Experience :

strong testing and code-quality practices.

Preferred :