Data Architect

New Era India; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

New Era India seeks a Senior Enterprise Data Architect to define and own the organization’s data architecture strategy and roadmaps in Bengaluru. You will design scalable data platforms, data lakes, warehouses, and lakehouse architectures across cloud platforms.

You will lead data modeling, ETL/ELT pipelines, data governance, security, and metadata, collaborating with Data Engineering, Data Science, and Business teams to align data solutions with strategic goals.

Qualifications

• 13-15 years of experience in Data Engineering or Data Architecture with leadership.
• Experience designing large-scale distributed data platforms and enterprise architectures.
• Strong knowledge of data modeling, data warehousing, data lakes, lakehouse, and distributed processing.
• Hands-on experience with Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, Redshift.
• Strong programming in Python, Java, or Scala.
• Strong SQL and database knowledge, relational and NoSQL.
• Experience with cloud platforms, preferably AWS.
• Experience designing real-time and batch data processing architectures.
• Understanding of microservices, APIs, distributed systems, and event-driven architectures.
• Experience with data governance, security, quality, lineage, metadata, and compliance.
• Understanding of data architecture patterns and best practices for scalability, availability, performance, and cost optimization.
• Experience with CI/CD, DevOps, infrastructure automation, and observability for data platforms.
• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.

Responsibilities

• Define and own the enterprise data architecture strategy, standards, and technology roadmap.
• Design scalable data platforms, data lakes, data warehouses, and lakehouse architectures.
• Develop architecture for batch and real-time data ingestion, processing, transformation, and integration.
• Evaluate and recommend data technologies, platforms, frameworks, and tools based on requirements.
• Design robust data models for operational and analytical workloads.
• Lead ETL/ELT pipelines, data integration frameworks, and data quality solutions.
• Architect solutions across cloud data platforms such as AWS/Azure/GCP and modern data technologies.
• Define data architecture patterns for analytics, Data Science, AI/ML, and GenAI use cases.
• Establish standards for data governance, security, privacy, metadata management, data lineage, and access control.
• Work with Data Engineering, Data Science, Product, Application Eng, and Business teams.
• Identify opportunities to improve data performance, scalability, reliability, cost efficiency.
• Lead architecture reviews and provide guidance for complex data engineering initiatives.
• Mentor senior engineers and contribute to building a strong data practice.
• Stay current with emerging technologies in cloud, distributed systems, data platforms, AI/ML, and GenAI.

Skills

Data architecture

Data engineering

Technical leadership

Cloud platforms

Distributed systems

Data modeling

Python

Java/Scala

SQL

CI/CD & DevOps

Tools

Spark

Kafka

Airflow

Databricks

Snowflake

BigQuery

Redshift

Job description

Role & responsibilities

• Define and own the enterprise data architecture strategy, principles, standards, and technology roadmap.
• Design scalable and highly available data platforms, data lakes, data warehouses, and lakehouse architectures.
• Develop architecture for batch and real-time data ingestion, processing, transformation, and integration.
• Evaluate and recommend appropriate data technologies, platforms, frameworks, and tools based on business and technical requirements.
• Design robust data models, including conceptual, logical, and physical data models for operational and analytical workloads.
• Drive implementation of ETL/ELT pipelines, data integration frameworks, and data quality solutions.
• Architect solutions across cloud data platforms such as AWS/Azure/GCP and modern data technologies.
• Define data architecture patterns for analytics, reporting, Data Science, AI/ML, and GenAI use cases.
• Establish standards for data governance, security, privacy, metadata management, data lineage, and access control.
• Work closely with Data Engineering, Data Science, Product, Application Engineering, and Business teams to ensure alignment between business objectives and data architecture.
• Identify opportunities to improve data performance, scalability, reliability, cost efficiency, and operational excellence.
• Lead architecture reviews and provide technical guidance for complex data engineering initiatives.
• Mentor senior engineers and architects and contribute to building a strong data engineering and architecture practice.
• Stay current with emerging technologies in cloud, distributed systems, data platforms, AI/ML, and GenAI and assess their applicability to the organization.

Preferred candidate profile

• 13-15 years of overall experience in Data Engineering, Data Architecture, or related areas, with significant experience in architecture and technical leadership.
• Strong experience designing large-scale, distributed data platforms and enterprise data architectures.
• Strong understanding of data modeling, data warehousing, data lakes, lakehouse architecture, and distributed data processing.
• Hands-on experience with technologies such as Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, Redshift, or equivalent platforms.
• Strong programming experience in Python, Java, or Scala.
• Strong knowledge of SQL and database technologies, including relational and NoSQL databases.
• Experience with cloud platforms, preferably AWS, with strong understanding of cloud-native data services.
• Experience designing real-time and batch data processing architectures.
• Strong understanding of microservices, APIs, distributed systems, and event-driven architectures.
• Experience with data governance, security, quality, lineage, metadata, and compliance.
• Strong understanding of data architecture patterns and best practices for scalability, availability, performance, and cost optimization.
• Experience working with CI/CD, DevOps, infrastructure automation, and observability for data platforms.
• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.