Data Engineer

Kforce has a client that is seeking a Data Engineer in Miami, FL. Responsibilities:

• Design, implement and support an analytical data infrastructure and working knowledge of Modern Data Warehouse concepts
• Design, build, and maintain efficient and scalable data pipelines and ETL processes to process large volumes of structured and unstructured data
• Optimize data storage and retrieval methods to ensure performance, scalability, and cost-efficiency
• Manage AWS resources including EC2, S3, Glue, Lambda, API's, IAM, Cloud Watch, etc.
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies
• Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
• Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis
• Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
• Maintain internal reporting platforms/tools including troubleshooting and development. Interact with internal users to establish and clarify requirements in order to develop report specifications
• Work with Engineering partners to help shape and implement the development of BI infrastructure including Data Warehousing, reporting and analytics platforms
• Contribute to the development of BI tools, skills, culture and impact
• Write advanced SQL queries and Python code to develop solutions
• Collaborate across teams to align AI initiatives with organizational goals and understanding of AI concepts
• Bachelor's degree in Computer Science, Information Technology, or a related field
• 3-5 years of experience in data engineering or a related role, with demonstrated success in delivering data solutions
• Experience with NoSQL databases, such as MongoDB, Cassandra, or DynamoDB, and an understanding of their appropriate use cases
• Experience with cloud platforms (AWS, Azure, GCP) and data services, such as AWS Redshift, Azure Synapse, or Google BigQuery
• Experience with data versioning and testing tools, such as DVC (Data Version Control) and dbt (data build tool)
• Knowledge of big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing
• Familiarity with data orchestration tools, such as Apache Airflow for scheduling and managing data workflows
• Understanding of data security practices, including encryption, access controls, and data masking
• Strong programming skills in Python, Java, or Scala, with experience in data processing frameworks (e.g., Apache Spark, Hadoop)
• AWS Glue, Lambda, S3, EC2, CloudWatch, Cloud Trail
• Dbt, Snowflake, Qlik
• Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning