Database Developer

Description

We are looking for a Staff Database Engineer to support the design, migration, and optimization of modern data platforms. This role focuses on moving database workloads into cloud environments, building reliable data pipelines, and enabling scalable access to large and complex datasets for downstream applications. The ideal candidate combines strong hands-on development skills with practical architecture knowledge across NoSQL, in-memory, and AI-oriented data technologies.

Responsibilities:

Lead database migration efforts into AWS-based environments, including planning, execution, and post-migration validation across modern data platforms.

Build and maintain data pipelines that process high-volume, complex information and deliver dependable outputs to consuming services and presentation layers.

Develop backend solutions using Python to automate database operations, streamline workflows, and improve data movement efficiency.

Work with DynamoDB, DocumentDB, and related NoSQL technologies to design scalable data models and support application performance needs.

Implement and optimize Redis, ElastiCache, MemoryDB, or similar in-memory data stores, including use cases tied to AI or vector-based workloads.

Partner with technical teams to translate architectural goals into practical cloud-based database implementations rather than high-level concepts alone.

Support search and data retrieval capabilities through technologies such as Solr and related indexing platforms used in production environments.

Create and maintain scripts for automation, deployment, and operational support using shell scripting and other command-line tools as needed.

Requirements

Proven experience migrating database solutions to AWS cloud environments in a hands-on technical capacity.
Strong working knowledge of DynamoDB, DocumentDB, and other NoSQL database technologies.
Advanced programming ability in Python for automation, integration, and data engineering tasks.
Experience building data pipelines that manage large-scale, complex datasets and feed downstream systems reliably.
Practical expertise with Redis or similar in-memory databases, ideally including AI, vector database, or low-latency application use cases.
Familiarity with Solr and search-oriented data platforms used to support high-volume environments.
Working knowledge of shell scripting for operational automation and support activities.
Ability to discuss and apply database architecture decisions in real implementation scenarios, not only at a conceptual level.

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