Data Engineer

Description

We are looking for a Data Engineer to build and enhance scalable data solutions that support analytics and business decision-making in Jacksonville, Florida. This role focuses on designing reliable warehouse structures, developing efficient data pipelines, and improving data quality across enterprise platforms. The ideal candidate brings strong technical depth in modern data engineering practices and is comfortable partnering with cross-functional teams to deliver well-governed, high-performing data assets.

Responsibilities:
Design and maintain enterprise data warehouse solutions using dimensional modeling techniques that support reporting, analytics, and long-term scalability.
Develop, orchestrate, and optimize ETL and ELT workflows for batch and near real-time data movement using modern pipeline and scheduling tools.
Build robust data architectures across cloud-based platforms while balancing performance, resilience, and cost efficiency.
Troubleshoot pipeline failures, data inconsistencies, and performance bottlenecks, then implement practical fixes that improve reliability and throughput.
Apply governance standards by supporting data quality controls, metadata practices, lineage visibility, and role-based access management.
Partner with business and technical stakeholders to translate data needs into well-structured solutions and provide clear updates on progress, risks, and delivery timelines.
Create efficient code for data transformation and aggregation using Python and advanced query development techniques.
Integrate data from databases, APIs, streaming platforms, and external sources to support a broad range of analytical use cases.
Mentor team members and contribute ideas that streamline processes, reduce friction, and strengthen engineering best practices.

Requirements

Bachelor's degree and at least 6 years of experience in data engineering, data architecture, analytics engineering, or software engineering with substantial data warehouse responsibility.
Demonstrated success building dimensional models such as star and snowflake schemas for large-scale warehouse environments.
At least 3 years of hands-on experience with a cloud data warehouse platform such as Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics.
Strong programming and data transformation skills using Python plus advanced SQL; Scala experience is also valuable.
Experience working with ETL or ELT frameworks and orchestration tools such as dbt, Informatica, Talend, Apache Airflow, or comparable technologies.
Familiarity with relational databases and preferably NoSQL platforms, along with experience handling large-volume structured and semi-structured data.
Understanding of data governance, access control, data quality practices, and performance optimization methods including partitioning, indexing, and workload tuning.
Experience with technologies such as Apache Spark, Apache Hadoop, Apache Kafka, and Microsoft Fabric, Synapse, or OneLake is preferred.

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