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.