Careers >Data Architect
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 DESCRIPTION
Job Title : Database Architect
Location: Trivandrum, Kerala
Type: On site, Full time
Pay: Competitive salary based on experience
We are seeking an experienced Database Architect with a strong focus on ETL, data lakes, and Graph SQL to design, implement, and optimize our data infrastructure for machine learning and AI applications. This role requires expertise in building scalable ETL pipelines, managing large-scale data environments, and supporting complex relational data structures, including graph databases, to power advanced ML and AI use cases.
JOB 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.
ETL Optimization: Streamline ETL workflows to reduce latency and enhance data transformation accuracy for ML applications that rely on rapid and high-volume data ingestion.
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.
Performance Tuning: Optimize database performance for both Graph SQL and relational databases to handle complex queries, large datasets, and low-latency requirements for real-time ML and AI applications.
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: Minimum of 5 years in database architecture, with hands-on experience in ETL, data integration, and pipeline management in data-intensive environments.
Graph SQL Proficiency: In-depth experience 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: Proficiency with ETL tools and frameworks (e.g., Apache NiFi, Talend, Airflow) and experience 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).
Database Management: Advanced skills in both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) with experience optimizing for large-scale machine learning applications.
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: Proven 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.
ADDITIONAL 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.
Fill this form to apply:
APPLY HERE
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 DESCRIPTION
Job Title : Database Architect
Location: Trivandrum, Kerala
Type: On site, Full time
Pay: Competitive salary based on experience
We are seeking an experienced Database Architect with a strong focus on ETL, data lakes, and Graph SQL to design, implement, and optimize our data infrastructure for machine learning and AI applications. This role requires expertise in building scalable ETL pipelines, managing large-scale data environments, and supporting complex relational data structures, including graph databases, to power advanced ML and AI use cases.
JOB 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.
ETL Optimization: Streamline ETL workflows to reduce latency and enhance data transformation accuracy for ML applications that rely on rapid and high-volume data ingestion.
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.
Performance Tuning: Optimize database performance for both Graph SQL and relational databases to handle complex queries, large datasets, and low-latency requirements for real-time ML and AI applications.
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: Minimum of 5 years in database architecture, with hands-on experience in ETL, data integration, and pipeline management in data-intensive environments.
Graph SQL Proficiency: In-depth experience 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: Proficiency with ETL tools and frameworks (e.g., Apache NiFi, Talend, Airflow) and experience 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).
Database Management: Advanced skills in both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) with experience optimizing for large-scale machine learning applications.
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: Proven 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.
ADDITIONAL 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.
Fill this form to apply:
APPLY HERE