Jellyfish Technologies - Data Engineer - Azure Databricks

Role : Data Engineer

Experience : 4-5 years.

Location : Remote.

About Us :

Jellyfish Technologies is a leader in leveraging cutting-edge data platforms to deliver innovative solutions.

We're a company dedicated to creating scalable and efficient data ecosystems.

We are seeking an experienced Databricks Architect to design and implement robust data solutions.

Position Summary :

- We are looking for a Data Engineer with 4-5 years of professional experience.

- The ideal candidate will have a strong foundation in data engineering principles, with hands-on experience in building, maintaining, and optimizing data pipelines.

- Additionally, you will be skilled in applying statistical and machine learning techniques to solve complex business problems.

- You will work on various projects, from designing data architectures to developing predictive models, all in a collaborative and fully remote environment.

Data Engineering :

- Design, develop, and maintain scalable and efficient data pipelines using ETL/ELT processes.

- Build and optimize data models to support data-driven decision-making and analytics.

- Manage and process large datasets from various sources, ensuring data quality and reliability.

- Work with cloud-based data platforms such as Databricks, AWS, Azure, or GCP.

- Troubleshoot and resolve data-related issues to ensure system stability and performance.

Technical Skills :

- Proficiency in programming languages like Python or Scala.

- Strong expertise in SQL and working with relational databases.

- Hands-on experience with big data technologies such as Apache Spark (Databricks is mandatory).

- Experience with cloud platforms (AWS, Azure, or GCP).

- Familiarity with data visualization tools (e.g., Tableau, Power BI, Matplotlib).

- Solid understanding of machine learning models and statistical analysis.

Preferred Qualifications :

- Experience with MLOps frameworks like MLflow.

- Knowledge of data warehousing solutions like Snowflake or Redshift.

- Background in developing and deploying production-level machine learning models.