Manhattan WMOS Engineer
ShyftLabs
Support Manhattan WMOS implementations and operations as a WMS Engineer at ShyftLabs. ShyftLabs builds data products and digital solutions for Fortune 500 companies.
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Valtech एक वैश्विक डिजिटल एजेंसी है, जो संगठनों को ग्राहक अनुभव बेहतर बनाने और अपने डिजिटल व्यवसायों को आगे बढ़ाने में मदद करती है। इसका काम प्रौद्योगिकी, मार्केटिंग, डेटा और एआई को एक साथ लाकर वाणिज्यिक पहलों, एंटरप्राइज़ डिजिटल रूपांतरण और बेहतर मार्केटिंग प्रदर्शन को समर्थन देता है। दुनिया भर में अपनी टीमों और कार्यालयों के साथ, Valtech डिजिटल रणनीति, अनुभव नवाचार और अधिक प्रभावी डिजिटल भविष्य गढ़ने के लिए डेटा के व्यावहारिक उपयोग पर कंपनियों के साथ साझेदारी करती है।
Senior Data Engineer – Portugal Remote
Senior Data Engineer at Valtech, developing cloud data platforms that support analytics, AI, and production data products. The role covers scalable pipelines, lakehouse architecture, GenAI, and machine learning systems.
विवरण
- Architect, develop, and improve cloud data platforms that support analytics, AI, and data products
- Create dependable batch, streaming, and near-real-time pipelines for structured and unstructured information
- Develop workflows for data ingestion, transformation, curation, and serving
- Implement lakehouse designs and medallion architecture with Databricks or Fabric
- Produce curated datasets for analytics, machine learning, causal modeling, optimization, GenAI, RAG, and agent-based applications
- Apply MLflow, Unity Catalog, Azure ML, and comparable platform tools to support production AI systems
- Create data models and coordinate workflows with Airflow, dbt, Databricks Workflows, Azure Data Factory, or comparable tools
- Establish practices for data governance, lineage, quality, access control, and observability
- Track data freshness, pipeline dependability, SLA performance, scalability, efficiency, and costs
- Provide APIs, feature inputs, and analytical endpoints for downstream machine learning and AI applications
- Clarify and refine business needs through stakeholder collaboration and analysis
- Work closely with data scientists, ML engineers, analysts, platform teams, and business stakeholders
- Help drive adoption of data products and advance data and AI practices
आवश्यकताएँ
- Hands-on expertise with Apache Spark and Delta Lake
- Strong Python and SQL programming skills
- Demonstrated experience building batch and streaming pipelines and production-grade data platforms
- Solid knowledge of data modeling, quality, and governance principles
- Hands-on experience with either AWS data services and Databricks Lakehouse, or Microsoft Azure/Fabric and related tools
- Experience with lakehouse architectures and distributed data systems
- Strong understanding of pipeline scalability, reliability, and performance
- Curiosity and well-developed problem-solving abilities
- Collaborative experience in cross-functional teams
- Experience in Agile or consulting environments is beneficial
- Snowflake or GCP familiarity is a plus, but not required
- Expertise across AWS and Azure/Fabric, GenAI and AI data systems, data-pipeline CI/CD, and infrastructure-as-code is desirable but not required
- Additional experience with Kafka, Spark optimization, advanced analytics and machine learning workloads, or large-scale analytics platforms is valuable
लाभ
- Remote and hybrid work flexibility, depending on country
- Career growth through international mobility and professional development programs
- Learning opportunities involving current tools, training, and industry experts
- Competitive compensation package
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