Data Engineer

Job Summary

We are looking for a Data Engineer to contribute to the design and implementation of modern cloud-native Data & AI platforms on AWS. You will work as part of a collaborative team to build production-grade architectures for startups and enterprise customers. This position combines technical delivery, hands-on engineering, and stakeholder collaboration across analytics environments and GenAI solutions.

Responsibilities

• Design and build scalable cloud-native data platforms and modern lakehouse architectures on AWS under the guidance of senior leadership.
• Build and maintain robust ETL/ELT pipelines, streaming architectures, and data services.
• Apply best practices for data modeling, orchestration, governance, and observability to ensure platform scalability.
• Assist in architecting modern AI solutions, including RAG pipelines, vector search, and ML integrations.
• Work closely with customers and internal teams to translate business requirements into functional technical designs.
• Participate in troubleshooting, code reviews, performance tuning, and production support to drive platform reliability.
• Support modernization projects involving the transition from legacy ETL systems to cloud-based data warehouses.

Requirements

• 4–6 years of hands-on experience in Data Engineering, Data Architecture, or Backend Engineering.
• Solid experience implementing cloud-native architectures and leveraging AWS data services.
• Strong hands-on experience with at least 4-5 of the following: Databricks, DBT, Snowflake, Airflow, Kafka, Spark, Redshift, S3, AWS Glue, or Lambda.
• Advanced SQL and Python development skills.
• Experience with data warehouse design, lakehouse architectures, and building scalable real-time data pipelines.
• Understanding of CI/CD, Infrastructure as Code (IaC), and monitoring practices.
• Excellent interpersonal skills with a "people person" attitude and the ability to engage in technical discussions with customers.
• A proactive, can-do attitude with a strong sense of ownership over assigned projects.

Advantages

• Experience with Amazon Bedrock, SageMaker, or vector databases.
• Exposure to Kubernetes or microservices architectures.
• Experience in high-scale environments such as fintech, gaming, or retail.
• Previous experience in a Professional Services or Managed Services environment.
• Familiarity with data quality and observability frameworks.