Job Description
About Giglithic – Pioneering the Future of Data‑Driven Travel Solutions Talentra is a global leader in innovative travel technology, delivering next‑generation platforms that connect millions of passengers with airlines, airports, and travel services worldwide. Our mission is to harness the power of data to create seamless, personalized experiences that redefine how people explore the world. As a fast‑growing, technology‑first organization, Flexoraq invests heavily in cutting‑edge cloud infrastructure, advanced analytics, and a culture of continuous learning. We are looking for visionary professionals who can help us turn massive, complex data sets into actionable insights – all from the comfort of their own home. Why This Role Is a Game‑Changer In today’s data‑centric ecosystem, the ability to model, preprocess, and store both structured and unstructured information is a strategic differentiator. As a Remote Data Modeling & Data Entry Specialist at arenaxflex, you will be at the heart of our data engineering engine, shaping the architecture that fuels our predictive models, real‑time dashboards, and AI‑driven recommendation systems. This is more than a data entry job – it is a critical, high‑impact position that blends rigorous engineering practices with creative problem‑solving. Core Responsibilities Design & Optimize Data Models: Create and refine relational, dimensional, and graph data models that support both transactional and analytical workloads. Preprocess Structured & Unstructured Data: Implement pipelines to cleanse, transform, and enrich data from diverse sources, including logs, sensor streams, and social media feeds. Cloud Data Warehouse Architecture: Lead the design of scalable, secure data warehouses on major cloud platforms (AWS, Azure, GCP), ensuring high performance and cost efficiency. Collaboration with Cross‑Functional Teams: Partner closely with product managers, software engineers, data scientists, and UX designers to translate business needs into technical solutions. Timeline Management: Deliver milestones on schedule, track progress with agile tools, and communicate status updates to stakeholders at all levels. Mentorship & Community Building: Share knowledge, contribute to internal tech forums, and mentor junior engineers, fostering a vibrant engineering community. Quality Assurance & Best Practices: Enforce engineering standards, conduct design reviews, implement automated testing, and maintain robust defect‑tracking processes. Research & Innovation: Explore emerging technologies (e.g., graph databases, streaming analytics), prototype proofs‑of‑concept, and recommend adoption strategies. Essential Qualifications & Experience Proven Track Record: Designed, developed, and supported complex software solutions in a production environment. SQL Mastery: Deep proficiency in writing optimized SQL queries, stored procedures, and performance tuning. Dimensional Modeling Expertise: Strong understanding of star/snowflake schemas, OLAP cubes, and multi‑dimensional analysis. Data Modeling Variety: Experience with graph, transactional, and operational data models is a distinct advantage. Full Product Lifecycle Familiarity: Hands‑on involvement in all stages—from requirements gathering, design, development, testing, to production support. Organizational Acumen: Excellent problem‑solving abilities, attention to detail, and the capacity to juggle multiple priorities. Cross‑Functional Collaboration: Ability to work independently with minimal supervision while influencing teams across the organization. Preferred Qualifications & Desirable Traits Experience with cloud‑native data warehouse services (e.g., Snowflake, Redshift, BigQuery). Familiarity with data‑pipeline orchestration tools such as Apache Airflow, Prefect, or Dagster. Exposure to big‑data processing frameworks (Spark, Flink) and NoSQL databases (Cassandra, MongoDB). A genuine curiosity for data science concepts and a desire to bridge engineering rigor with analytical experimentation. Research mindset: capable of turning abstract ideas into structured experiments, prototypes, and production implementations. Strong communication skills—both written and verbal—and a passion for mentoring peers. Resilience and a proactive attitude toward overcoming technical challenges and ambiguity. Skills & Competencies for Success Analytical Thinking: Ability to dissect complex datasets, identify patterns, and translate insights into tangible business value. Technical Versatility: Comfortable switching between SQL, Python/Scala for data wrangling, and scripting for automation. Cloud Literacy: Knowledge of IAM, networking, and cost‑management best practices in cloud environments. Collaboration Tools: Proficiency with Git, Jira, Confluence, and modern CI/CD pipelines. Documentation Discipline: Produce clear, concise technical documentation for data models, pipelines, and APIs. Customer‑Focused Outlook: Recognize how data engineering decisions impact end‑user
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