Coffeebeans.io - Solutions Architect - Data Engineering

Description :

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 :

- 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 :

- 10+ 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.