Enterprise Analytics Data Engineer Job Tampa area,Florida USA,IT/Tech

Title: Enterprise Analytics Data Engineer

Company: Tampa Electric Company

Location: Midtown East Tower, Tampa, Florida

Shift: 8 Hr. X 5 Days

Recruiter: Mark Koener

Position Concept

Data Architects responsible for the "back end" components of the BIA environment from standards, architecture, and design perspectives. Overall responsibility for the implementation and support of the data movement and engineering processes required to populate the organization’s custom‑built analytic data structures (staging areas, data warehouse and data marts) as well as the design of these structures. Responsible for strategy and design related to federation of custom‑built data architecture with vendor‑provided solutions.

Provides technical oversight and assures quality of work done by data analysts and data engineers and developers. Serves as a subject matter expert with significant, recent hands‑on experience with state‑of‑the‑art data integration and cleansing tools available in the SAP and Azure platforms. Responsible for assuring that data integration and cleansing tool suites are effectively deployed and applications are developed based on industry and vendor‑specified best practices.

Responsibility for ensuring the organization’s analytic data architecture effectively enables needed applications and develops standards in data modeling, metadata management, data integration and master data management.

Focus Areas

Expert‑level SQL, Python, and ETL/ELT pipeline design using tools like dbt, Fivetran, and Airbyte. Proficient in both batch and streaming architectures to support high‑volume enterprise workloads.
Hands‑on experience with cloud data warehouses (Snowflake, Big Query, Redshift) and lakehouse platforms such as Databricks and Delta Lake. Ability to build and maintain scalable, cost‑optimized infrastructure using Terraform or equivalent IaC tooling.
Strong foundation in dimensional modeling, star schema design, and slowly changing dimensions. Experience implementing enterprise‑grade semantic layers and dbt best practices across staging, marts, and reusable macros.
Proficient in workflow orchestration tools (Airflow, Prefect, or Dagster) with experience building CI/CD pipelines for data products. Comfortable with containerization (Docker, Kubernetes) and managing SLA‑driven production environments.

Primary Duties and Responsibilities

Define and evolve the data architecture in alignment with enterprise standards to support diverse use cases such as reporting, OLAP, dashboards, data mining, and statistical analysis. Ensure the architecture meets operational requirements including scalability, security, reliability, and flexibility. Determine the appropriate deployment environment, whether cloud‑based or on‑premise. (25%)

The Data Architect, in alignment with enterprise standards, is responsible for establishing best practices for source data analysis, ETL design and documentation, data federation across platforms (e.g., AMI, Empower, custom analytics), business rule placement (with the Information Architect), ETL testing and release management (with the Change Coordinator), database‑level security (with DBAs), and data masking, anonymization, and tokenization. (15%)

The Data Architect translates user requirements into data models and supporting applications by guiding data analysts in source analysis, advising ETL developers, and designing logical and physical analytic data structures. Responsibilities include deploying metadata tools to document data sources, analytic structures, semantic layers, and BI applications; implementing security for data integration and cleansing tools; collaborating on support environments and release management best practices;

overseeing the operational environment; and working with DBAs and operations teams to monitor and optimize ETL and data structure performance. (15%)

Accountable for the quality of data model and ETL application deliverables and so is responsible for establishing and managing QA related functions including:
Testing (unit, system, stress, etc.) and Peer reviews (e.g., reviews of data models, ETL application design, etc.). (15%)

Provides technical oversight of the…