Note: The job is a remote job and is open to candidates in USA. Kunai builds full-stack technology solutions for banks, credit and payment networks, infrastructure providers, and their customers. The company is seeking a Senior Data Engineer to build and optimize Python- and AWS DynamoDB-based data pipelines and systems for financial services platforms, supporting batch and streaming workloads, large-scale analytics, data quality, and real-time insights.
Responsibilities
- Design, build, and maintain scalable, reliable, and high-performance data pipelines using Python and AWS DynamoDB
- Implement robust ETL/ELT processes for both batch and streaming data workloads
- Collaborate with data scientists, analysts, and backend engineers to ensure data accessibility and integrity
- Develop and optimize DynamoDB data models and indexing strategies to meet diverse access patterns and high throughput requirements
- Leverage cloud-native data storage solutions and serverless architecture to drive cost-efficient and scalable systems
- Contribute to the implementation and tuning of distributed data processing frameworks such as Apache Spark to support large-scale data transformations and analytics, where applicable
- Monitor and optimize pipeline performance, scalability, and latency
- Establish data validation, monitoring, and alerting mechanisms to ensure pipeline reliability and accuracy
- Collaborate with stakeholders on data governance, compliance, and security best practices
- Participate in architectural discussions and code reviews, advocating for best practices in Python development and cloud data engineering
- Mentor junior team members and promote knowledge sharing across the engineering organization
Skills
- 5+ years of professional experience in data engineering or software engineering roles focused on data-centric solutions
- Expert Python programming skills with a strong focus on data processing and pipeline development
- Deep experience designing and implementing solutions with AWS DynamoDB, including data modeling, performance tuning, and capacity planning
- Thorough understanding of cloud computing fundamentals (AWS preferred)
- Solid experience building high-scale ETL/ELT pipelines and working with streaming and batch data
- Familiarity with distributed data processing frameworks; experience with Apache Spark is a significant plus but not required
- Experience working with NoSQL databases such as DynamoDB, Cassandra, or similar
- Comfortable with schema design trade-offs and query pattern optimizations in key-value and document stores
- Track record of collaborating effectively in cross-functional teams involving product, data science, and operations
- Strong verbal and written communication skills; able to explain technical concepts clearly to non-technical stakeholders
- Ability to thrive in fast-paced, evolving environments with changing requirements
- At this time, we are unable to provide sponsorship for this role
- Bachelor's Degree, in lieu of a degree, demonstrating in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in technology for each missing year of college is required
- Hands-on experience with Apache Spark or other big data processing frameworks
- Familiarity with other AWS services such as Lambda, Kinesis, Glue, or Redshift
- Prior experience working in financial services or payments industry data platforms
- Exposure to containerized/cloud-native deployments (e.g., Docker, Kubernetes)
- Experience with CI/CD pipelines and infrastructure-as-code tools such as Terraform or CloudFormation
Benefits
- Professional development opportunities
- Flexible work arrangements
Company Overview