As a Senior Data Engineer, you will be responsible for building and maintaining the infrastructure that supports data collection, processing, and storage, working closely with data scientists, analysts, and other stakeholders to ensure that data systems are reliable, scalable, and secure. Your work will be crucial in enabling data-driven decision-making across the organization. This is a key technical role focused on developing and optimizing the company's data infrastructure which involves designing and implementing data pipelines, ensuring data quality, and collaborating with cross-functional teams to support various data initiatives.
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Responsibilities
- Design, construct, install, test, and maintain highly scalable data pipelines with a focus on machine learning models and analytics.
- Work closely with data scientists, ML engineers, and stakeholders to ensure that data is accessible, consistent, and reliable for ongoing projects.
- Develop and maintain APIs for data access and manipulation, and integrate with external data services as needed.
- Manage and optimize data storage solutions for both structured and unstructured data.
- Implement processes to monitor data quality and ensure production data is always accurate and available for key stakeholders.
- Collaborate with ML engineers to assist in data-related technical issues and provide architectural guidance and solutions.
- Ensure compliance with data security and privacy policies.
- Maintain clear and up-to-date documentation including data dictionaries, metadata, and architectural diagrams.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; or equivalent work experience.
- 7+ years of experience in a Data Engineering role.
- Proficiency in programming languages like Python and SQL, with experience managing large scale data.
- Hands-on experience with big data technologies like Spark and Flink.
- Familiarity with machine learning frameworks such as TensorFlow or PyTorch.
- Strong understanding of data warehousing or Lake-house concepts, ETL processes, and data modeling.
- Experience with API development and integration with data services.
- Experience with cloud platforms like Azure.
- Knowledge in DevOps, CI/CD methods, and containerization technologies like Docker or Kubernetes.
- Experience with real-time/streaming data processing.
Technical Stack
- Programming Languages: Python, SQL
- Big Data Technologies: Spark, Flink
- Databases: Clickhouse, MySQL, PostgreSQL, MongoDB, Cassandra, HBase, Redis
- Cloud Platforms: Azure
- API Development: RESTful APIs, GraphQL
- Data Services: Kafka, RabbitMQ
- Containers: Docker, Kubernetes
