Key Responsibilities:

● Develop, test, and maintain data architectures, including databases, data lakes, and data warehouses.

● Design and implement scalable and reliable ETL/ELT pipelines to ingest, transform, and load data from various sources.

● Lead technical integration efforts to connect new external platforms and data sources in highly scalable fashions.

● Optimize and improve data processing workflows for performance, scalability, and cost-effectiveness.

● Ensure data integrity, consistency, and security across all data platforms.

● Collaborate with cross-functional teams to understand data needs and provide efficient solutions.

● Provide actionable insights for improving performance of relational databases.

● Monitor and troubleshoot data pipelines, ensuring timely resolution of issues.

● Implement best practices for data governance, metadata management, and documentation.

● Work with cloud-based data platforms and leverage services such as S3, Redshift, Snowflake, or similar technologies.

Key Requirements:

● Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field.

● 5 years of experience in data engineering or a similar role.

● Experience with Snowflake.

● Proficiency in Python.

● Proficiency in SQL and experience working with relational databases (PostgreSQL, MySQL, SQL Server)

● Proven experience in data pipeline development and designing large-scale high throughput data integration systems

● Hands-on experience with ETL/ELT tools like Apache Airflow or similar tools.

● Experience with AWS and related data services.

● Knowledge of data modeling, warehousing concepts, and best practices.

● Experience integrating data solutions into a REST API.

● Understanding of CI/CD pipelines and DevOps practices for data infrastructure.

● Strong problem-solving skills and ability to work in a fast-paced environment.

Plus Requirements:

● Experience with NoSQL databases like DynamoDB or MongoDB.

● Familiarity with data streaming technologies such as Apache Kafka or AWS Kinesis.

● Experience working with containerized applications using Docker or Kubernetes.

● Knowledge of machine learning model deployment and MLOps concepts.

● Experience with machine learning.