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Popular mid-level Data Engineer role but specialized Snowflake/Databricks/dbt stack, moderate competition.
Platform-specific Snowflake/Databricks/dbt requirements moderately limit cross-industry portability.
Explicit 1–4 years plus mandatory Snowflake, Databricks, dbt/Matillion skills make shortlisting strict.
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Design, build, and optimize scalable ELT data pipelines using Snowflake, Azure Databricks (PySpark/Spark SQL), dbt, and Matillion DPC.
Own full lifecycle delivery of ingestion, transformation, and orchestration workflows covering multiple data sources, ensuring high-quality and reusable data assets aligned to layered architectures (medallion, staging, marts).
Collaborate with cross-functional teams (data analysts, scientists, architects) in agile, client-facing consulting settings, driving production support, performance tuning, and documentation.
1 to 4 years experience in data engineering roles with at least 6 months hands-on experience in Snowflake and dbt.
Mandatory hands-on experience with Azure Databricks including development and deployment of PySpark/Spark SQL pipelines, Delta Lake tables, notebooks, jobs, and Databricks Workflows in production.
Experience with Matillion Data Productivity Cloud for data ingestion and orchestration.
Strong SQL skills, familiarity with Git-based CI/CD workflows, and experience with orchestration tools such as dbt Cloud, Airflow, Azure Data Factory, or Databricks Workflows.
Experienced building modular, production-ready data pipelines in cloud data platforms emphasizing Snowflake and Azure Databricks with proficiency in PySpark and dbt.
Comfortable working in fast-paced, client-facing consulting environments, owning end-to-end pipeline delivery and collaborating with geographically distributed data teams.
Strong operational focus on pipeline performance tuning, data quality validation, documentation, and adherence to best practices for modern data architectures (medallion/lakehouse).