As a Senior Data Engineer, you will be responsible for designing, developing, and maintaining advanced, scalable data systems that power critical business decisions. You will lead the development of robust data pipelines, ensure data quality and governance, and collaborate across cross-functional teams to deliver high-performance data platforms in production environments. This role requires a deep understanding of modern data engineering practices, real-time processing, and cloud-native solutions.
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Responsibilities:
- Data Pipeline Development & Management: Design, implement, and maintain scalable and reliable data pipelines. Ensure data integrity, timeliness, and accuracy across systems. Implement data quality tools and validation frameworks.
- Data Processing & Optimization: Build high-performance systems using techniques like data denormalization, partitioning, caching, and parallel processing. Develop stream-processing applications using Apache Kafka.
- Cloud, Infrastructure, and Platform Engineering: Develop and deploy data workflows on AWS or GCP, using services such as S3, Redshift, Pub/Sub, or BigQuery. Collaborate with platform teams for scalability and observability of data pipelines.
- Database Engineering: Write and optimize complex SQL queries on relational and NoSQL databases. Work with the ELK stack for search, logging, and real-time analytics.
- Data Governance & Stewardship: Implement robust data governance, access control, and stewardship policies. Establish metadata management and auditability across pipelines.
- Machine Learning & Advanced Analytics Enablement: Collaborate with data scientists for ML models, ensuring data readiness for experimentation and deployment.
- Documentation & Continuous Improvement: Maintain thorough documentation and continuously evaluate and improve the data engineering stack.
Qualifications:
- 8+ years of experience in data engineering within a production environment.
- Advanced knowledge of Python and Linux shell scripting for data manipulation and automation.
- Strong expertise in SQL/NoSQL databases such as PostgreSQL and MongoDB.
- Experience building stream processing systems using Apache Kafka.
- Proficiency with Docker and Kubernetes in deploying containerized data workflows.
- Good understanding of cloud services (AWS or Azure).
- Hands-on experience with ELK stack (Elasticsearch, Logstash, Kibana).
Education:
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
Preferred Experience:
- Working knowledge of data quality tools and data observability solutions.
- Understanding of data governance frameworks and enterprise compliance protocols.
- Exposure to CI/CD pipelines for data deployments.
Why AI71:
- Mission-driven work on cutting-edge AI applications with a talented and passionate team.
- Unparalleled opportunity to innovate and solve real-world challenges.
- Competitive compensation, benefits, and significant career growth opportunities.
- A flexible working environment with the latest tools and technologies.
