Data Engineer II
Occupations:
Data ScientistsData Warehousing SpecialistsSoftware DevelopersDatabase ArchitectsComputer Systems Engineers/ArchitectsIndustries:
Web Search Portals, Libraries, Archives, and Other Information ServicesEducational Support ServicesMedia Streaming Distribution Services, Social Networks, and Other Media Networks and Content ProvidersOther Professional, Scientific, and Technical ServicesBusiness Support ServicesJob Description
Data Engineer OpportunityLocation & Work ArrangementLocation: Miami, FLType: On-site / Remote (Hybrid required by end of year)Contract Duration: 1+ YearLevel: Mid-Level (Senior candidates not required)Role OverviewJoin a fast-growing Data Engineering & Analytics team. The role involves developing high-performance data solutions, algorithms, and workflows for vast datasets (billions of transactions) gathered from retail, restaurants, banks, and consumer-focused companies. You will work with front-end visualizations and machine learning techniques to drive business insights.ConsultingKey ResponsibilitiesData Architecture & Engineering: Design data architecture/schema, build automated data pipelines, and optimize machine learning frameworks. Machine Learning & AI: Design ML systems and AI software to automate predictive models; ensure algorithm accuracy for user recommendations.Data Processing: Transform unstructured data into useful information (e.g., auto-tagging images, text-to-speech); handle real-time, streaming, batch, and API-based data ingestion.Problem Solving: Solve complex problems with multi-layered datasets and optimize existing libraries.Collaboration: Advise Data Scientists and consumers on data issues, partner with cross-functional teams (engineering, sales, consultants) to prioritize problems, and evaluate trade-offs for analytics solutions.Data Governance: Implement and validate data lineage, quality checks, and classification policies.Innovation: Experiment with new tools to streamline pipeline development/testing and continuously identify new technical approaches.Project Management: Break large solutions into releasable milestones, incorporate stakeholder feedback, and track usage metrics.Maintenance: Maintain awareness of technical trends and escalate technical errors/bugs.Technical Skills & ToolsLanguages: Python, RBig Data Execution Engines: Hive, Impala, SparkAreas of Expertise: Machine Learning, Artificial Intelligence, Data Architecture, Data Lineage, Big Data Optimization