Data Architect

Engineering

|

(Databricks | AWS | AI/RAG)

Job Description

We are looking for an experienced Data Architect to lead the design and evolution of a modern data platform supporting critical business applications within the healthcare industry.

This is a highly strategic role where you will define the data architecture, establish engineering best practices, and provide technical leadership to ensure scalable, reliable, and high-quality data solutions. You will work closely with engineering teams, product stakeholders, and client leadership to transform complex business requirements into robust data architectures.

Beyond designing data pipelines, you will serve as the technical authority for data engineering decisions, helping shape the long-term architecture while mentoring engineers and driving engineering excellence.

Responsibilities

Design and evolve scalable, cloud-native data architectures on AWS.

Define data models, integration patterns, and architecture standards across multiple data sources.
Lead the design of modern data pipelines using Databricks, Spark, Python, and SQL.
Establish best practices for data quality, governance, scalability, performance, and observability.
Guide Data Engineers through technical leadership, architecture reviews, and mentoring.
Partner with product managers, business stakeholders, and client teams to understand business needs and translate them into technical solutions.
Design efficient ingestion, transformation, and orchestration strategies for large-scale datasets.
Collaborate on AI-ready data platforms supporting Retrieval-Augmented Generation (RAG) and Generative AI initiatives.
Drive technical decisions that improve reliability, maintainability, and long-term scalability of the platform.

Requirements

7+ years of experience in Data Engineering, Data Architecture, or related roles.

Proven experience designing enterprise-scale data architectures.
5+ years of work experience with Azure Databricks
Strong hands-on experience with Databricks.
Expert-level Python and SQL.
Strong experience with Apache Spark.
Experience building cloud-native solutions on AWS.
Experience with AWS services such as S3, Glue, Athena, and related data services.
Experience with workflow orchestration tools such as Airflow.
Strong understanding of data modeling, ETL/ELT design, and distributed data processing.
Experience designing highly scalable and reliable data platforms.
Strong communication skills with experience working directly with clients and business stakeholders.
Experience leading technical initiatives and mentoring engineering teams.
Advanced English Level.

Preferred Skills

Experience with AI/ML data platforms.

Familiarity with Retrieval-Augmented Generation (RAG) architectures.
Experience working with Vector Databases (Pinecone, Weaviate, Chroma, Qdrant, Milvus, or similar).
Knowledge of LangChain, LlamaIndex, or similar frameworks.
Experience with Docker and Kubernetes.
Experience implementing CI/CD and Infrastructure as Code.
Background working in consulting or client-facing environments.