Full Stack Data Solutions Engineer

1 day ago


Philadelphia, Pennsylvania, United States rockITdata Full time
Job Overview

We are seeking a highly skilled Full Stack Data Solutions Engineer to join our team at rockITdata. This exciting opportunity offers the chance to work on diverse data projects, building end-to-end data solutions that drive innovation and growth for our clients.

In this role, you will design and implement scalable data ingestion pipelines, integrate data from various systems and platforms, and develop efficient data storage solutions. You will also apply data transformation techniques, such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), to clean, transform, and enrich raw data. Additionally, you will build software applications and APIs to expose data services and functionality to other systems and applications, ensuring seamless operations and enhanced customer engagement.

This is a Remote position, offering flexibility and autonomy in your work environment. If you have a strong background in both data engineering and software development, with expertise in building end-to-end data solutions, we encourage you to apply for this challenging and rewarding role.

The ideal candidate will have a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, and proficiency in programming languages such as Python, Java, or Scala. Strong understanding of database concepts, data modeling techniques, and SQL programming is essential, along with hands-on experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of data warehousing concepts and technologies, such as Redshift, BigQuery, Snowflake, and familiarity with version control systems, Agile methodologies, and CI/CD practices, are also desirable.

Key Responsibilities
  • Data Ingestion and Integration: Design and implement scalable data ingestion pipelines to efficiently collect and process data from various sources, integrating data from different systems and platforms to create unified datasets for analysis and reporting.
  • Data Storage and Management: Develop and maintain data storage solutions such as data lakes, data warehouses, and NoSQL databases, optimizing data storage and retrieval mechanisms for performance, scalability, and cost-effectiveness.
  • Data Processing and Transformation: Implement data processing workflows for cleaning, transforming, and enriching raw data into usable formats, applying data transformation techniques such as ETL and ELT processes.
  • Data Modeling and Optimization: Design and implement data models to support analytical and reporting requirements, optimizing data models for query performance, data integrity, and storage efficiency.
  • Software Development and Integration: Build software applications and APIs to expose data services and functionality to other systems and applications, integrating data engineering workflows with existing software systems and platforms.

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