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.

Solutions Architect Microservices and Data