Top Data & Analytics Jobs in Little Rock,

The Data Integration Specialist will work with clients to onboard their data to Enterra solutions, validate data processes, and communicate requirements. Responsibilities include mapping data, addressing gaps, conducting initial data wrangling, and testing solutions. The role emphasizes collaboration with data engineers and understanding client data needs.

As a Senior Consultant in Data & Analytics, you will lead clients in their analytics journey, develop strategies for data preparation, create predictive models, and provide insights through visual analytics and reporting. You will also conduct interviews for gap analysis and design data flows.

The Data Engineer Consultant at DAS42 is responsible for developing data integrations, implementing data warehouses and ETL pipelines, and optimizing clients' data environments. They work collaboratively on projects, document client data environments, and support the pre-sales process while utilizing technologies like Python and SQL alongside data visualization and cloud platforms.

As a Data Scientist I, you will help the data team by identifying and managing data sources, utilizing Python, SQL, and R for data scraping and visualization. You will automate data processing, develop machine learning tools, collaborate with various teams, and advocate for best practices in data analysis and visualization.

The Cyber Threat Hunt Analyst is responsible for creating threat models, planning threat hunt missions, analyzing logs, and reporting risk analysis. This role involves collaborating with teams to enhance threat detection and security posture. Ideal candidates should have experience in network and host analysis and proficiency in scripting languages.

As an Ad Hoc Data Processing/Data Entry Specialist, responsible for accurately entering and processing data according to company's standards and procedures. Requires high attention to detail, efficiency, and handling confidential information.

The role involves architecting, building, and maintaining reliable data pipelines, developing data applications in Python and SQL, ensuring data quality, and optimizing software engineering techniques while collaborating with IT and business teams.