Data Engineer (Python)

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Data Engineer (Python)

Company Overview

Our client is a dynamic and innovative organization operating in a high-stakes domain, leveraging cutting-edge data technologies to drive critical insights and solutions. They foster a collaborative and growth-oriented environment where talented individuals can make a significant impact.

Job Description

Responsibilities:

• Data Pipeline Development & Optimization:
  • Design, develop, and maintain scalable data pipelines for real-time and batch processing.
  • Implement efficient data extraction, transformation, and loading (ETL) processes.
  • Optimize data workflows for performance, scalability, and reliability.
• Graph Database & Network Analysis:
  • Work with graph databases such as Neo4j and NetworkX to model and analyze complex relationships.
  • Develop and optimize graph-based data structures for efficient querying and analysis.
  • Implement algorithms for network analysis, anomaly detection, and pattern recognition.
• Graph Database Management & Data Modeling:
  • Design and implement graph database solutions tailored for data engineering use cases.
  • Develop data models that support efficient data storage, retrieval, and analysis within graph databases.
  • Ensure data integrity, consistency, and security through best practices in graph database management.
• Big Data & Streaming Frameworks:
  • Work with big data frameworks such as Hadoop and Spark for large-scale data processing.
  • Develop and optimize data lake architectures to support efficient data storage and retrieval.
  • Utilize streaming frameworks such as Apache Kafka and Apache Flink for real-time data processing.
• Collaboration & Cross-Functional Integration:
  • Work closely with data scientists, analysts, and software engineers to translate business requirements into technical solutions.
  • Collaborate on the integration of data pipelines with machine learning models and analytical applications.
  • Provide technical mentorship and guidance to junior team members.
• Data Quality & Governance:
  • Implement data validation, monitoring, and governance strategies to ensure high data quality.
  • Establish best practices for data lineage, documentation, and version control.
  • Ensure compliance with data privacy and security policies.

Requirements:

• Experience:
  • 5+ years of professional experience in data engineering or related fields.
  • Proven experience in designing and maintaining large-scale data pipelines and processing frameworks.
  • Experience working with graph databases such as Neo4j and tools like NetworkX is required.
  • Experience in cybersecurity, finance, healthcare, or other high-stakes domains is a plus.
• Skills:
  • Proficiency in Python, with strong expertise in data manipulation libraries (Pandas, NumPy, Pytorch Geometric, Networkx, etc.).
  • Strong understanding of graph databases, network analysis, and related algorithms.
  • Expertise in Cypher query language for graph database operations.
  • Experience with big data frameworks such as Hadoop and Spark.
  • Knowledge of data lakes architecture for scalable data storage and retrieval.
  • Experience with streaming frameworks like Apache Kafka or Apache Flink for real-time data processing.
• Software Development & Engineering:
  • Strong understanding of data structures, algorithms, and distributed computing concepts.
  • Experience with API development and integration.
  • Familiarity with version control (Git) and automated testing frameworks.

Preferred Qualifications:

• Experience in designing and deploying graph-based data solutions.
• Certifications in data engineering, big data technologies, or database management.
• Knowledge of cybersecurity data analysis and anomaly detection techniques.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals in Singapore.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
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