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.