Database developer Jobs in Aurora, CO

Position: Principal GO Developer

Location: Denver, CO

Duration: 1 Year

Job Description:

• We are looking for more experienced (above 12 years) and played lead / architect roles for this position.
• We are seeking a Senior Engineer to own and evolve a production-critical payment platform responsible for high-volume financial transactions.
• This role is focused on stability, correctness, and long-term system stewardship, not greenfield development.
• In addition to strong programming expertise, this role requires experience embedding AI/ML capabilities into enterprise systems and SDLC processes, supporting AI-driven transformation initiatives such as intelligent automation, anomaly detection, and data-driven decisioning within transactional platforms.
• The ideal candidate has deep experience operating Tier 1 systems where reliability, data integrity, and performance are non-negotiable.
• They demonstrate strong engineering judgment, maintain a disciplined and low-complexity approach to design, and are capable of driving meaningful improvements independently within an existing distributed system.
• This role requires a hands-on engineer who can lead through execution, make sound technical decisions under real-world constraints, and contribute to the long-term evolution of a high-value platform, including selective adoption of AI/ML capabilities where they improve system outcomes without compromising stability.

Role Context

The system is a Go-based, microservice-oriented payment platform running in a

containerized environment with a MySQL backend.

It supports high-throughput transaction processing and significant annual revenue,

making reliability and correctness critical.

The platform originated externally and requires thoughtful, incremental

improvement rather than wholesale redesign.

The work involves ongoing evolution of the system, including improving data

fidelity, observability, performance, and flexibility while maintaining strict

operational stability.

The platform is progressively incorporating AI/ML-driven capabilities, requiring

careful integration into existing system boundaries and SDLC practices.

Key Responsibilities

Own and evolve existing Go-based services, making measured, low-risk changes

that improve system capability, reliability, and clarity.

Design and implement backend functionality for payment processing, transaction

routing, reconciliation, and financial data movement with a strong focus on

correctness and auditability.

Integrate AI/ML components into the existing architecture (e.g., model inference

services, decision engines, data pipelines) while ensuring system stability and

auditability.

Apply AI/ML across the SDLC, including:

o Intelligent test generation and validation

o Production anomaly detection and incident prediction

o Log and metric analysis using ML techniques

o Code quality and defect prediction tools

Collaborate with data science teams to operationalize ML models (deployment,

monitoring, versioning, rollback strategies) within a production-grade environment.

Proactively identify system limitations, operational risks, and data gaps, and drive

pragmatic solutions with minimal oversight.

Evaluate the impact and risk of changes in a high-throughput transactional

environment, ensuring safe and controlled rollouts.

Improve system observability, data capture, and operational insight to support

both engineering and business needs, including ML-driven observability

enhancements.

Model and optimize transactional data using relational databases, with careful

attention to consistency, performance, and maintainability.

Build and maintain APIs and service interfaces with clear contracts and long-term

usability in mind.

Contribute to AI transformation initiatives, identifying where AI/ML can

meaningfully enhance system reliability, fraud detection, operational efficiency, or

decision-making.

Collaborate with product, architecture, and non-technical stakeholders to ensure

solutions meet business, operational, regulatory, and ethical AI governance

requirements.

Provide technical leadership by guiding design decisions, reviewing code, and

mentoring engineers while maintaining a focus on simplicity and stability.

Own services through their full lifecycle, including design, deployment, monitoring,

incident response, and continuous improvement, including ML lifecycle management

(MLOps).

Mandatory Skills: GO/Golang, MySQL/Oracle/PostgreSQL, AL/ML, AWS

Optional Skills: Java, Python, Kubernetes, Docker

Required Qualifications

12 plus years of experience in software engineering, demonstrated experience

building and operating production-critical backend systems with meaningful business

or revenue impact.

Strong hands-on experience with Go/Golang or Java based technologies in real-

world production environments.

Experience integrating AI/ML solutions into enterprise systems, including

deploying, consuming, or operationalizing ML models.

Understanding of AI/ML concepts such as supervised/unsupervised learning, model

evaluation, inference pipelines, and data quality considerations.

Experience applying AI/ML within SDLC processes (e.g., testing, monitoring,

observability, or developer productivity tooling).

Deep experience with relational databases (e.g., MySQL, PostgreSQL, Oracle),

including schema design, transactional modeling, and performance optimization.

Strong understanding of transactional systems, including data integrity,

idempotency, reconciliation, auditability, and failure handling.

Proven ability to make pragmatic, low-complexity design decisions in distributed

systems.

Experience working within and improving existing systems with real-world

constraints, rather than primarily greenfield environments.

Ability to independently identify problems, define solutions, and drive execution to

completion.

Strong communication skills, including the ability to work effectively with both

technical and non-technical stakeholders.

Preferred Qualifications

Experience with payment systems, financial transaction platforms, or other high-

integrity data domains.

Experience supporting systems with high transaction volume, low latency

requirements, or strict uptime expectations.

Familiarity with MLOps practices, including model deployment, monitoring, drift

detection, and lifecycle management.

Experience with real-time or near real-time ML inference systems in production

environments.

Familiarity with secure data handling, privacy considerations, and ethical AI

practices in regulated environments.

Experience with containerized or cloud-based deployment environments (e.g.,

Kubernetes, Docker, or similar platforms).

Exposure to event-driven or asynchronous processing patterns.

Ideal Candidate Profile

Operates as a true owner, not a task executor; identifies issues and drives solutions

independently.

Demonstrates restraint and discipline in system design, avoiding unnecessary

abstraction and complexity.

Comfortable working in high-stakes environments where stability and correctness

take precedence over novelty.

Able to apply AI/ML pragmatically, focusing on measurable improvements rather

than experimentation for its own sake.

Calm, methodical, and reliable under production pressure.

Able to collaborate effectively with senior peers while mentoring and guiding

junior engineers.

Focused on building systems that are understandable, maintainable, resilient, and

progressively intelligent over time.

Expectations from this role:

Performs tests in strict compliance with detailed instructions for the following:

1. Ensure that new or revised components or systems perform to expectation.

2. Ensure meeting of standards; including usability, performance, reliability or

compatibility.

3. Document Test results and report defects

Typical performance measures:

1. Timely completion of all tasks

2. # of test cases/script executed in comparison to the benchmarks

3. # of valid defects

Performance Areas:

Test Design, Development, Execution:

Execute test cases / scripts

Identify, log and track defects

Retest

Log in productivity data

Requirements Management:

Participate, Seek Clarification, Understand, Review

Domain relevance:

Test features and components with good understanding of the business problem

being addressed for the client

Manage knowledge:

Consume, Contribute

Skill Examples:

1. Ability to review user story / requirements to identify ambiguities

2. Ability to design test cases / scripts as per user story / requirements

3. Ability to apply techniques to design efficient test cases / script

4. Ability to set up test data and execute tests

5. Ability to identify anomalies and detail them

Knowledge Examples:

1. Knowledge of Testing Methodologies

2. Knowledge of Tools

3. Knowledge of Types of testing

4. Knowledge of Testing Processes

5. Knowledge of Testing Standards