Homeoffice Full Stack Data Engineer at rockITdata
rockITdata · Arlington, United States Of America · Remote
- Professional
Driven by Innovation and built on Trust, rockITdata is a unique SDVOSB services company that partners with leading commercial healthcare/life sciences organizations on cutting edge innovations - think AI, automation and data transformation. We then bring those commercially tested solutions to government entities to deliver predictable, measurable impact for the American taxpayer and consumer.
We are seeking a talented and experienced Full Stack Data Engineer to join our team. The ideal candidate will have a strong background in both data engineering and software development, with expertise in building end-to-end data solutions. This role offers an exciting opportunity to work on diverse data projects and contribute to the development of innovative data-driven applications. This is a Remote position.
Key Responsibilities
Required Qualifications
Preferred Qualifications
We are seeking a talented and experienced Full Stack Data Engineer to join our team. The ideal candidate will have a strong background in both data engineering and software development, with expertise in building end-to-end data solutions. This role offers an exciting opportunity to work on diverse data projects and contribute to the development of innovative data-driven applications. This is a Remote position.
Key Responsibilities
- Data Ingestion and Integration:
- Design and implement scalable data ingestion pipelines to efficiently collect and process data from various sources.
- Integrate 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.
- Optimize 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.
- Apply data transformation techniques such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes.
- Data Modeling and Optimization:
- Design and implement data models to support analytical and reporting requirements.
- Optimize 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.
- Integrate data engineering workflows with existing software systems and platforms.
- Monitoring and Maintenance:
- Establish monitoring and alerting mechanisms to track the health and performance of data pipelines and systems.
- Conduct regular maintenance activities to ensure the reliability, availability, and scalability of data infrastructure.
- Documentation and Collaboration:
- Document data engineering processes, architectures, and solutions to facilitate knowledge sharing and collaboration.
- Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders to understand requirements and deliver solutions.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Proficiency in programming languages such as Python, Java, or Scala for data engineering and software development.
- Strong understanding of database concepts, data modeling techniques, and SQL programming.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP for building and deploying data solutions.
- Knowledge of data warehousing concepts and technologies (e.g., Redshift, BigQuery, Snowflake).
- Familiarity with version control systems (e.g., Git) and software development best practices (e.g., Agile, CI/CD).
Preferred Qualifications
- Experience building solutions for Commercial clients in Pharma, Biotech, CPG, Retail or Manufacturing industries.
- Experience with containerization technologies such as Docker and orchestration tools like Kubernetes.
- Knowledge of streaming data processing frameworks (e.g., Apache Flink, Apache Kafka Streams).
- Familiarity with data governance and security practices for protecting sensitive data.
- Strong problem-solving skills and the ability to troubleshoot complex data engineering issues.
- Excellent communication skills and the ability to collaborate effectively in a team environment.
