Data Engineer

Job Overview: As a Data Engineer, you will be responsible for designing, implementing, and maintaining scalable data pipelines and infrastructure to support the organization's data needs. You will work closely with data scientists, analysts, and other stakeholders to ensure that data is collected, processed, and made accessible for analysis and decision-making purposes. This role requires a combination of technical expertise in Data Engineering tools and platforms, as well as a solid understanding of data architecture and best practices.

Responsibilities:

1. Data Pipeline Development: Design, build, and maintain robust, scalable data pipelines to ingest, process, and transform large volumes of structured and unstructured data from diverse sources.
2. Data Modeling: Develop and implement data models and schemas to organize and structure data for analytical purposes, ensuring data quality, integrity, and consistency.
3. Data Integration: Integrate data from various internal and external sources, including databases, APIs, and streaming platforms, to support business intelligence, analytics, and reporting requirements.
4. Performance Optimization: Optimize data pipelines and infrastructure for performance, reliability, and efficiency, including tuning database queries, improving ETL processes, and implementing caching strategies.
5. Data Governance and Compliance: Implement data governance policies and procedures to ensure data privacy, security, and regulatory compliance, such as GDPR or CCPA.
6. Collaboration: Collaborate with cross-functional teams, including data scientists, analysts, software engineers, and business stakeholders, to understand data requirements and deliver solutions that meet business needs.
7. Documentation and Training: Document Data Engineering processes, workflows, and best practices, and provide training and support to other team members as needed.
8. Continuous Improvement: Stay up-to-date with emerging technologies, trends, and best practices in Data Engineering, and propose and implement improvements to data infrastructure and processes.

Qualifications:

1. Education: Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field. Advanced degrees or certifications in Data Engineering or big data technologies are a plus.
2. Technical Skills:
   • Proficiency in programming languages such as Python, Java, or Scala, with experience in data processing frameworks like Apache Spark or Apache Flink.
   • Strong SQL skills and experience working with relational and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).
   • Familiarity with distributed computing and cloud platforms, such as AWS, Google Cloud Platform, or Microsoft Azure.
   • Experience with data pipeline orchestration tools like Apache Airflow, Luigi, or Apache NiFi.
   • Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
   • Understanding of data warehousing concepts and technologies (e.g., Snowflake, Redshift, BigQuery).
3. Problem-Solving Skills: Ability to analyze complex Data Engineering problems, identify alternative solutions, and implement effective strategies to address them.
4. Communication Skills: Strong verbal and written communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders.
5. Teamwork: Demonstrated ability to work collaboratively in a team environment, sharing knowledge and expertise to achieve common goals.
6. Attention to Detail: Meticulous attention to detail and a commitment to ensuring data accuracy, consistency, and integrity throughout the data lifecycle.
7. Adaptability: Flexibility and adaptability to work in a fast-paced, dynamic environment with evolving priorities and requirements.
8. Problem-Solving Skills: Ability to troubleshoot and debug complex Data Engineering issues, and implement effective solutions in a timely manner.