Data Engineer

Senior Data Engineer

Key Responsibilities

1. Data Infrastructure Design and Development

• Design and implement robust, scalable, and efficient data pipelines to support business analytics and decision-making.
• Develop, test, and maintain ETL/ELT workflows using modern tools and technologies.
• Ensure data quality, consistency, and reliability across all pipelines and systems.

2. Data Architecture and Modelling

• Build and optimize data architectures for large-scale, distributed systems.
• Develop data models and schemas to support analytical and operational reporting.
• Design strategies for data warehousing and data lake integration.

3. Technology and Tools Management

• Leverage cloud platforms (e.g., AWS, Azure, GCP) to build and manage scalable solutions.
• Work with big data technologies such as Hadoop, Spark, Kafka, or similar tools.
• Manage relational and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).

4. Collaboration and Mentorship

• Collaborate with data scientists, analysts, and software engineers to meet organizational goals.
• Mentor and guide junior engineers, promoting best practices in coding, architecture, and data management.
• Partner with stakeholders to translate business requirements into technical solutions.

5. Performance Optimization and Security

• Monitor and improve data pipeline performance, addressing bottlenecks and inefficiencies.
• Implement security best practices to ensure the safety and privacy of data.
• Automate routine processes to reduce manual intervention and operational overhead.

Required Skills and Qualifications

• Education

  : Bachelors or Master's degree in Computer Science, Data Engineering, or a related field.

• Experience

  :
• 3-8 years of experience in data engineering or related roles.
• Proven expertise in designing and building large-scale data pipelines and architectures.

• Technical Skills

  :
• Proficiency in programming languages like Python, Java, or Scala.
• Advanced SQL skills for complex queries and performance tuning.
• Hands-on experience with cloud platforms (AWS, Azure, GCP).
• Experience with big data tools (e.g., Spark, Hadoop, Kafka).
• Knowledge of containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes, Airflow).

• Soft Skills

  :
• Strong problem-solving abilities and attention to detail.
• Excellent communication and collaboration skills.
• Ability to lead projects and work independently in a fast-paced environment.

Preferred Qualifications

• Certification in cloud platforms like AWS Certified Data Analytics Specialty, Google Professional Data Engineer, or Azure Data Engineer Associate.
• Experience with streaming data pipelines and real-time processing.
• Familiarity with machine learning workflows and tools.
• Knowledge of data governance and compliance standards.