Design and optimize dimensional models; structure facts/dimensions for performance and reuse. Develop and maintain ELT/streaming pipelines (batch + near-real-time). Write and tune advanced SQL; implement orchestration and dependency management. Build integration interfaces (APIs/microservices) and manage data contracts. Enforce data-quality checks, validation rules, and observability/alerting. Use Git and CI/CD to promote code and data assets across environments. Support production run: incident/problem triage, performance monitoring, SLA follow-up. Collaborate with stakeholders to refine requirements and document lineage/logic.

Strong SQL and Python for data engineering (pipelines, transformations, utilities). Solid Data Warehousing & Dimensional Modeling (star/snowflake, facts/dimensions). Experience building ELT and/or streaming pipelines, including orchestration. Hands-on with version control (Git) and basic CI/CD for data workloads. Working knowledge of data-quality practices and observability/monitoring. Comfortable designing/consuming APIs and managing data contracts. Exposure to cloud data platforms (Azure/AWS/GCP) and container/orchestration basics. Clear communication, collaborative mindset, problem-solving, and ownership. NICE-TO-HAVE ELT & orchestration tools (dbt, Airflow, Dagster, Fivetran). Distributed processing & streaming (Spark, Databricks, Kafka). Cloud warehousing/lakehouse (Snowflake, BigQuery, Redshift) and cost/perf tuning. Data testing & quality frameworks (Great Expectations, Soda). IaC/DevOps (Terraform, Helm) and Kubernetes. Security & governance (secrets, RBAC, catalogs/lineage).