Data Modeler – Advanced - 26-01015

NavitasPartners•Tallahassee, FL

13h•Hybrid

About The Position

We are seeking a highly experienced Data Modeler – Advanced to lead the design, optimization, and governance of enterprise-grade data pipelines, data models, and ETL workflows. This role requires deep technical expertise, leadership capability, and the ability to drive high standards in data quality, performance, and governance.

Requirements

3–5 years of experience in data engineering , including advanced ETL and data pipeline design (Level 5)
Expert proficiency in Python and SQL (Level 5)
Exceptional analytical and problem-solving skills with the ability to derive actionable insights from complex datasets (Level 5)
Expert knowledge of relational database design and data modeling principles (Level 5)
Extensive experience with data warehouses, data lakes, and/or data lakehouse architectures (Level 5)
Strong ability to prioritize work and ensure timely, high-quality delivery (Level 5)
Proven ability to work independently and lead initiatives (Level 4)
Strong collaboration and relationship-building skills (Level 4)
Effective verbal and written communication skills (Level 4)

Nice To Haves

5–10 years of hands-on experience with Alteryx Designer
Familiarity with scientific, environmental, or regulated data domains
Experience with business intelligence tools such as Qlik Sense or similar platforms

Responsibilities

Design, implement, and maintain scalable and reliable data pipelines and architectures using Alteryx Designer.
Develop and maintain logical data models using Oracle SQL Developer Data Modeler or equivalent tools.
Read, write, update, and manage structured datasets across multiple platforms.
Create, maintain, and govern ETL code repositories following industry best practices.
Perform ad hoc data cleansing and complex data transformations as required.
Define, implement, and enforce data quality control procedures , standards, and metrics.
Execute monitoring procedures to ensure accuracy, completeness, and consistency of datasets.
Identify data anomalies and lead root cause analysis and remediation planning .
Optimize data processing workflows and algorithms for performance, scalability, and reliability .
Ensure adherence to data privacy regulations and security best practices across data lifecycle activities.
Monitor and tune data systems, resolving performance bottlenecks through query optimization, indexing, and caching strategies .
Transform raw data into analytics-ready formats using cleansing, aggregation, filtering, and enrichment techniques .
Establish and maintain data and algorithm governance frameworks supporting analytics, reporting, and automated decision-making.
Collaborate closely with data scientists, analysts, and technical stakeholders to enhance data quality, security, and governance .
Stay current with emerging technologies, tools, and methodologies in data engineering and analytics.
Provide technical leadership, guidance, and mentorship to junior team members, fostering a culture of excellence, innovation, and continuous learning.