Senior Data Engineer

This is a remote position.

● Design, develop, and maintain scalable, secure, and cost-effective data infrastructure on cloud platforms such as AWS, GCP, or Azure.
● Implement cloud-native solutions like data lakes, warehouses (e.g., Snowflake, BigQuery, Redshift), and serverless computing. Build, deploy, and manage real-time and batch data pipelines using tools such as Apache Airflow, Apache Kafka, or cloud- native orchestration solutions.
● Optimize ETL/ELT processes for seamless data ingestion, transformation, and integration from diverse sources. Ensure high performance, scalability, and cost-efficiency of cloud data solutions through tuning and capacity planning.
● Implement caching strategies and data partitioning techniques for large-scale datasets. Enforce best practices for data privacy, security, and regulatory compliance (e.g., GDPR, PCI DSS).
● Implement identity management, encryption, and monitoring tools to safeguard sensitive data. Work closely with cross-functional teams to understand business requirements and deliver cloud-based data solutions.
● Mentor junior data engineers and contribute to team knowledge sharing. Evaluate emerging cloud and big data technologies to recommend enhancements to our data infrastructure.
● Drive automation in deployment, testing, and monitoring using CI/CD pipelines and Infrastructure-as-Code (e.g., Terraform, CloudFormation).

Requirements

● Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. 5+ years of data engineering experience with at least 3 years working on cloud-based solutions.
● Proven experience in the fintech domain is a significant advantage. Strong expertise in cloud platforms such as AWS (S3, Redshift, Glue), GCP (BigQuery, Dataflow), or Azure (Data Factory, Synapse Analytics).
● Proficiency in programming languages like Python, Java, or Scala.
● Solid knowledge of database systems and SQL, including NoSQL technologies (e.g., DynamoDB, MongoDB).
● Experience with big data tools such as Apache Spark, Hadoop, or Databricks.
● Strong familiarity with containerization (Docker) and orchestration (Kubernetes).
● Hands-on experience with Infrastructure-as-Code tools (Terraform, CloudFormation)