Senior Data Engineer

Kunai

About The Position

Kunai builds full-stack technology solutions for banks, credit and payment networks, infrastructure providers, and their customers. Together, we are changing the world’s relationship with financial services. At Kunai, we help our clients modernize, capitalize on emerging trends, and evolve their business for the coming decades by remaining tech-agnostic and human-centered. We are seeking a Senior Data Engineer with deep Python and DynamoDB expertise to join a high-impact data engineering program supporting the modernization and scaling of financial services platforms. In this hands-on role, you will build and optimize data pipelines and systems that ingest, process, and analyze vast volumes of transactional and operational data, enabling real-time insights and driving data-driven decision-making at scale. Experience with Spark is a strong plus but not required.

Requirements

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.

Nice To Haves

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.

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.