Intern

**About Us**:
We are at the forefront of innovation, developing cutting-edge AI-based decision intelligence products designed to transform data into actionable insights. We are driven by a passion for technology and a commitment to delivering solutions that empower organizations to navigate complex decision-making environments with precision and foresight.

**Job Summary**:
**Key Responsibilities**:

- Data Pipeline & ETL Design: Architect and implement efficient ETL pipelines to ingest, transform, and load data from various sources into structured, unstructured, and semi-structured formats to support ML/AI processes.
- Graph SQL Modeling: Develop and maintain graph-based data models (e.g., Neo4j, Amazon Neptune) that can effectively represent relationships within data, supporting AI-driven recommendations, knowledge graphs, and complex data linkages.
- Data Lake & Warehouse Management: Oversee data lake and data warehouse environments to store and manage large volumes of data, ensuring efficient organization and accessibility for analysis and ML model development.
- Data Modeling: Create and manage flexible data models tailored to machine learning needs, including feature stores, time-series data, and entity-relationship structures.
- Collaboration with ML Teams: Partner closely with data scientists, ML engineers, and analysts to understand data needs, structure data efficiently, and enable data flow for model training and inference.
- Data Integration & Quality: Ensure reliable data integration from multiple sources with robust quality checks, data validation, and ongoing monitoring to maintain data integrity.
- Documentation & Standards: Develop and maintain documentation for ETL processes, data lake management, and Graph SQL models to support scalability and reproducibility.
- Data Governance: Implement and enforce data governance, cataloging, and best practices to ensure compliance with regulatory and security standards.

**Qualifications**:

- Experience in database architecture, with hands-on experience in ETL, data integration, and pipeline management in data-intensive environments.
- Graph SQL : Should be familiar with graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph) and the ability to model, query, and optimize graph structures to support machine learning tasks.
- ETL Expertise: Should know how to work with ETL tools and frameworks (e.g., Apache NiFi, Talend, Airflow) and in automating ETL workflows across diverse data sources.
- Big Data & Data Lakes: Strong knowledge of data lake architectures and big data frameworks (e.g., Hadoop, Spark, Kafka) and experience with cloud-based data lake solutions (e.g., AWS S3, Azure Data Lake, GCP BigQuery).
- Programming Skills: Proficiency in a programming language commonly used for ETL and data manipulation (e.g., Python, SQL) and experience with data orchestration.
- Performance Optimization: experience in optimizing ETL pipelines, graph databases, and large-scale data storage to ensure high throughput and efficient data access.
- Cloud Experience: Experience working with cloud-based data services (AWS, GCP, Azure) and knowledge of managing ETL and data lakes in cloud environments.
- Communication Skills: Strong ability to communicate technical concepts clearly to stakeholders across data science, engineering, and product teams.

**Preferred Qualifications**:

- Certifications: Relevant certifications in data engineering, cloud data services, or graph databases (e.g., AWS Certified Database, Neo4j Certified Professional).
- ML/AI Pipeline Experience: Familiarity with the unique data processing needs of ML pipelines, including experience with feature engineering and real-time model serving.
- Knowledge of MLOps Tools: Experience with MLOps platforms (e.g., MLflow, Kubeflow) to integrate ETL and data transformation processes into machine learning workflows.

**Job Types**: Full-time, Fresher, Internship

Application Question(s):

- When dealing with complex, highly customized data and/or when working with large volumes of continuously changing data, from your own experiences, what DB architecture and platforms would you prefer? Please substantiate your answer by giving instances from a recent project that you worked on.
- Within a startup, what level of involvement would you be looking for in the platform selection, infrastructure installation & configuration and database design?
- From your personal experience, what is the biggest challenge you have faced as a DB Architect? Please provide the answer with details.
- What is the most important value to have as an ideal DB Architect and why?

Work Location: In person