Data Engineer II / AWS Data Engineer

Job Title: Data Engineer II / AWS Data Engineer
Location: Miami, Florida or Irving, Texas (Hybrid)
Duration: Contract to Hire

Manager: Finance and land domain Analytics engineer in practice

What the person will do. Own transformation and modeling work across finance and land initiatives. The team is rebuilding the client's financial reporting data stack and preparing consolidated data assets for internal agentic and MCP-based access.

Day-to-day split. Approximately 80% dbt/transformation and data-modeling work and 20% collaboration with business stakeholders. This is not a ticket-taking role; someone who can receive a project, think critically, and run with it.

Must Haves

• Strong dbt development using SQL and Jinja.
  
• Strong SQL and Python fundamentals.
  
• Snowflake experience and an understanding of how modeled data supports reusable data products.
  
• Data modeling and semantic-model experience, not merely extraction and loading.
  
• Critical thinking, adaptability, project ownership, and comfort working directly with stakeholders.
  

Helpful but Flexible

• AWS Glue and AWS data-lake experience. Azure is acceptable if the candidate understands data-flow and lake concepts.
  
• Finance, accounting, cash, P&L, or land-data experience shortens the learning curve.
  
• Iceberg and experience exposing or loading transformed data into Snowflake.
  
• Power BI awareness, although Rob is intentionally moving away from producing many custom reports.
  
• Interest in agentic data products and MCP servers.
  

Reject or Probe Carefully

• Traditional ETL engineers who are strong in ingestion but light on dbt, dimensional/semantic modeling, or business context.
  
• BI-only candidates whose main strength is Power BI report development.
  
• Candidates who depend on scripted or AI-fed interview answers and cannot reason through a new scenario.
  

 
Your Responsibilities on the Team 

• 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, CloudWatch, 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 the BI tools, skills, culture, and impact.
  
• Write advanced SQL queries and Python code to develop solutions.
  
• Working Knowledge of Snowflake. 
  
• Collaborate across teams to align AI initiatives with organizational goals and an understanding of AI concepts
  
• Knowledge of continuous integration/continuous delivery (CI/CD) pipelines and working on deployments when necessary.
  

Requirements

• 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.
  
• AWS Glue, Lambda, S3, EC2, CloudWatch, Cloud Trail.
  
• Dbt, Snowflake, SQL, Python, Qlik.
  
• Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning.
  
•  Experience with NoSQL databases, such as MongoDB, Cassandra, or DynamoDB, and an understanding of their appropriate use cases.
  
• Strong programming skills in Python, Java, or Scala, with experience in data processing frameworks (e.g., Apache Spark, Hadoop).
  
• Experience with cloud platforms (AWS, Azure, GCP) and data services, such as AWS Redshift, Azure Synapse, or Google BigQuery.
  
• 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.
  
• Experience with data versioning and testing tools, such as DVC (Data Version Control) and dbt (data build tool).
  
• Understanding of data security practices, including encryption, access controls, and data masking.