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Mid-level demanders in metros but specialization reduces applicant density.
Medium because core data engineering skills transfer, but platform specialization (Snowflake/Databricks) limits cross-industry fit.
High due to explicit 5+ years and mandatory hands-on Snowflake or Databricks plus specific tech stack.
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Design, develop, optimize, and maintain scalable and high-performance data pipelines and solutions using Snowflake and/or Databricks.
Implement and tune complex ETL/ELT processes, including SQL and PySpark transformations, with focus on data quality, performance, and observability.
Collaborate closely with data engineers, data scientists, analysts, and business teams to deliver reliable data solutions on cloud platforms (AWS, Azure, or GCP).
5+ years of experience in data engineering with hands-on expertise in at least one of Snowflake or Databricks.
Bachelor’s or Master’s degree in Computer Science, IT, Data Engineering, or related field.
Strong proficiency in SQL and Python; Spark experience mandatory for Databricks exposure.
Experience building and optimizing data pipelines on cloud platforms (AWS, Azure, or GCP) with ETL/ELT processes and orchestration tools (e.g., Airflow, Azure Data Factory, AWS Glue).
Experienced in hands-on development and optimization using Snowflake and/or Databricks covering data modelling, query performance, CDC, schema evolution, and pipeline observability.
Skilled at translating complex business requirements into robust, scalable data solutions with a focus on data quality and operational reliability.
Works effectively in cross-functional teams including data scientists and analysts and contributes to code reviews and best practices in a cloud data engineering environment.