Database Developer

Description

We are looking for a Database Engineer to support the design, migration, and optimization of cloud-based data platforms. This role focuses on building reliable database solutions for large and complex datasets while enabling efficient delivery of information to applications, analytics tools, and other downstream consumers. The ideal candidate brings strong hands-on experience with cloud database technologies, Python development, and modern data engineering practices, along with the ability to contribute practical architectural insight.

Responsibilities:

Lead the migration of database workloads to cloud-based environments, with particular emphasis on DynamoDB and DocumentDB implementations.

Design, build, and maintain scalable data pipelines that process high-volume datasets and deliver dependable data to downstream services and presentation layers.

Develop and optimize Python-based solutions to support database operations, automation, and data movement across distributed systems.

Create and maintain database structures and access patterns for NoSQL and in-memory platforms, including Redis and related AI or vector-driven data use cases.

Partner with engineering teams to define practical database architecture decisions and translate technical requirements into effective cloud implementations.

Support search and retrieval capabilities by working with Solr and other data access technologies used across the environment.

Implement scripting and automation tasks using shell scripting where needed to streamline deployment, maintenance, and operational workflows.

Evaluate and integrate technologies such as ElastiCache, MemoryDB, MongoDB, and vector databases to meet performance, scalability, and application need.

Requirements

Proven experience developing and supporting database solutions in cloud environments, including hands-on migration work.

Strong working knowledge of DynamoDB, DocumentDB, and related AWS database services.

Advanced Python skills for data engineering, automation, and backend database development.

Experience building data pipelines for large, complex datasets in production environments.

Practical expertise with Redis, especially in use cases involving AI workloads, in-memory processing, or vector-based data storage.

Familiarity with Solr and NoSQL technologies such as MongoDB or comparable platforms.

Ability to discuss and apply database architecture decisions in real-world implementations rather than only at a conceptual level.

Experience with shell scripting is preferred.

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