Must have

• Databricks

• Data engineering

• SQL

• Python

• ETL

• Spark

• Communication skills

• English (C1)

Nice to have

• BigData

• Azure

• Airflow

• dbt

• Snowflake

Requirements description

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
• 5+ years of experience in data engineering or related fields, with a focus on large-scale data systems
• 3+ years of experience with Databricks
• Proficiency in SQL and experience with databases such as MySQL, PostgreSQL, or NoSQL databases
• Strong programming skills in languages such as Python, Java, or Scala.
• Experience with ETL tools and frameworks and data pipeline orchestration.
• Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (AWS, GCP, Azure).
• Strong understanding of data warehousing concepts, data modeling, and schema design.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills and ability to work effectively in a team-oriented environment

Offer description

Description

Entrada AI, Inc. is looking for a highly skilled Senior Databricks Engineer to join our growing team of consultants. In this role, you will be responsible for designing, constructing, and optimizing large-scale data processing systems that enable advanced analytics and business intelligence. You will collaborate closely with data scientists, analysts, and other engineers to ensure the availability, reliability, and efficiency of our client’s Databricks solution.

Your responsibilities

1. Design, develop, and maintain scalable and robust data pipelines that collect, process, and store large volumes of structured and unstructured data.
2. Architect and implement data warehouses, data lakes, and other storage solutions that support analytics and reporting needs.
3. Optimize data architectures and workflows for performance, scalability, and cost-efficiency.
4. Collaborate with data scientists and analysts to understand data requirements and ensure that data systems meet their needs.
5. Ensure data quality, integrity, and security by implementing best practices in data governance and management.
6. Develop and maintain documentation for data systems, including data models, flow diagrams, and operational procedures.

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