Principal Engineer

About Us

CoffeeBeans Consulting is a tech consulting firm focused on making organizations

AI-ready

by structuring their data efficiently across various sources and enabling AI-driven solutions. We specialize in

data architecture, pipelines, governance, MLOps, and Gen AI

solutions, helping clients achieve

faster go-to-market and cost efficiency .

We are looking for a Principal Engineer who can design and implement complex solutions that are scalable, future-proof and AI led.

Key Responsibilities

Solution Architecture

Engage with clients to understand business goals, technical constraints, and AI / data maturity.

Translate requirements into

scalable solution blueprints

across tech stack and infrastructure.

Expert in microservices and design patterns.

data, analytics, AI, and cloud .

Define

end-to-end architecture

covering ingestion, storage, processing, analytics, ML / AI, and visualization.

Ensure designs meet

security, compliance, and governance

requirements.

Data Architecture & Engineering

Architect

modern data platforms

(data lakes, lakehouses, warehouses) using tools like Databricks, Snowflake, BigQuery, Redshift, etc.

Design robust

ETL / ELT pipelines , streaming architectures, and

real-time analytics

solutions.

Define

data models, metadata management, and master data strategies .

Implement

data governance frameworks

(e.g., CDMC, DAMA DMBOK).

AI & MLOps Enablement

Integrate

ML / Gen AI solutions

into the data platform architecture.

Work with Data Scientists to

operationalize AI models

using MLOps best practices.

Evaluate

LLM integration , vector databases, and AI agents within enterprise architectures.

Leadership & Delivery

Provide

technical leadership

to engineering teams.

Review designs, code, and deployment plans for quality and scalability.

Work closely with client stakeholders, delivery managers, and CTO to ensure success.

Required Skills & Experience

12+ years

in data engineering, data architecture, or solution architecture roles.

Hands-on expertise in

cloud platforms

(AWS, GCP, Azure) and

IaC

(Terraform, CloudFormation).

Strong knowledge of

data modeling ,

SQL / NoSQL databases ,

streaming tech

(Kafka, Pub / Sub, Kinesis).

Proven experience with

Databricks, Snowflake, or equivalent

modern data stack tools.

Knowledge of

data governance, security, and compliance

frameworks.

Familiarity with

AI / ML architectures , MLOps tools (MLflow, Kubeflow), and Gen AI frameworks (LangChain, RAG pipelines).

Strong communication and client-facing skills.