Cloud Data Engineer (Snowflake/Databricks)

Accountabilities

• • Design, develop, and maintain scalable ETL/ELT data pipelines that reliably process and transform large volumes of data.
  • Build, optimize, and maintain data transformation workflows using Snowflake and/or Databricks.
  • Develop effective data modeling strategies, including star schemas, lakehouse architectures, and other scalable approaches.
  • Optimize query performance, data processing efficiency, and cloud infrastructure costs.
  • Implement and maintain workflow orchestration using Airflow or comparable orchestration technologies.
  • Develop reliable datasets and data products that support analytics, business intelligence, and reporting teams.
  • Establish and maintain data quality, governance, monitoring, and reliability practices across data pipelines and platforms.
  • Work with batch and streaming data processing technologies to support evolving data requirements.
  • Collaborate with analytics, BI, engineering, and other cross-functional stakeholders to understand requirements and deliver effective data solutions.
  • Continuously improve data platform architecture, pipeline reliability, performance, scalability, and operational efficiency.

  Requirements

  • 4+ years of professional experience in Data Engineering or a closely related field.
  • Strong proficiency in SQL and Python, with experience applying both to production-grade data engineering solutions.
  • Hands-on experience working with Snowflake and/or Databricks.
  • Practical experience with Apache Spark, including batch and/or streaming data processing.
  • Proven experience designing and implementing ETL/ELT pipelines.
  • Familiarity with Apache Airflow or similar data orchestration technologies.
  • Experience working with at least one major cloud platform, including AWS, Azure, or Google Cloud Platform (GCP).
  • Strong understanding of data modeling principles and experience designing scalable analytical data structures.
  • Knowledge of data quality, governance, monitoring, and reliability practices.
  • Experience with dbt or comparable data transformation tools is preferred.
  • Experience with real-time streaming technologies such as Kafka, Kinesis, or Pub/Sub is an advantage.
  • Familiarity with BI tools and downstream analytics use cases is a plus.
  • Strong analytical and problem-solving abilities, with the capacity to work effectively across technical and business requirements.
  • Strong communication and collaboration skills, with the ability to work effectively with engineering, analytics, and BI stakeholders.

  Benefits

  • Full-time opportunity within a modern cloud data engineering environment.
  • Fully remote work arrangement.
  • Opportunity to work extensively with Snowflake, Databricks, Spark, SQL, Python, and cloud technologies.
  • Exposure to modern ETL/ELT, data modeling, orchestration, transformation, and analytics architectures.
  • Opportunity to contribute to scalable data platforms supporting advanced analytics and business intelligence.
  • Cross-functional collaboration with data engineering, analytics, BI, and technology teams.
  • Opportunity to develop expertise across AWS, Azure, and/or GCP cloud environments.
  • Hands-on experience with modern data engineering practices and technologies.