Data Architect

Key Responsibilities:

• Monitor and maintain

  data pipeline reliability

  , including logging, alerting, and troubleshooting failures.
• Good knowledge on Artificial Intelligence and Machine learning
• Design, develop, and optimize

  relational and NoSQL databases

  for diverse applications, including AI and large-scale data processing.
• Build and manage

  ETL/ELT pipelines

  to ensure efficient data processing and transformation.
• Optimize database performance for

  high-availability applications, reporting, and analytics

  .
• Implement

  data partitioning, indexing, and sharding

  strategies for scalability.
• Ensure

  data integrity, governance, and security

  across multiple applications.
• Collaborate with teams to streamline data access, model storage, and training workflows when applicable.
• Develop

  SQL queries, stored procedures, and views

  for efficient data retrieval.
• Monitor and troubleshoot

  database performance, bottlenecks, and failures

  .

Required Skills & Qualifications:

• Strong SQL expertise

  (writing complex queries, optimization, stored procedures, indexing).
• Experience with

  relational databases

  (PostgreSQL, SQL Server) and

  NoSQL databases

  (MongoDB, Redis).
• Knowledge of

  AI-related database optimizations

  , such as vector databases (e.g., Pinecone, FAISS, Weaviate) for

  embedding storage and retrieval

  is a plus.
• Experience working with

  enterprise data workflows

  , including data modeling and architecture.

• Dimensional Modeling / Data Warehousing

  : Experience with dimensional modeling (star/snowflake schemas) and data warehousing concepts (e.g., Kimball, Inmon).

• Metadata Management & Data Catalogs

  : Familiarity with metadata management, data catalogs, or data lineage tools (e.g., Alation, Data Catalog in GCP, AWS Glue Data Catalog).
• Hands-on experience with

  cloud platforms

  (AWS, Azure, GCP) and cloud-based data storage solutions.
• Familiarity with

  big data technologies

  (Spark, Hadoop, Kafka) is a plus.
• Strong

  Python

  or

  SQL scripting

  skills for data manipulation and automation.
• Knowledge of

  data security, privacy regulations (GDPR, CCPA), and compliance standards

  .

• Unit / Integration Testing

  : Experience with testing data pipelines, including unit and integration testing for transformations.

• Documentation

  : Strong documentation practices for pipelines, database schemas, and data governance processes.
• Excellent problem-solving skills and ability to

  collaborate with cross-functional teams

  .
• Experience with

  workflow orchestration tools

  like Apache Airflow or Prefect.

Preferred Qualifications:

• Experience with

  vector databases

  and

  retrieval-augmented generation (RAG)

  workflows.
• Understanding of

  AI model storage, caching, and retrieval

  from databases when applicable.
• Experience in

  machine learning model feature engineering and ML model versioning

  .
• Experience with

  containerization technologies

  like Docker or Kubernetes for deploying data solutions.

• Data Quality and Observability Tools

  : Experience with tools or frameworks for data quality checks, validation, and data observability (e.g., Great Expectations, Monte Carlo, Databand).

  Role & responsibilities

Preferred candidate profile