Lead Data Architect

EdgeVerve; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

EdgeVerve Systems Limited seeks an experienced data architect to own end-to-end data architecture, modeling, and governance across transactional and analytical domains. You will design data platforms, pipelines, and integrations with NiFi and related tools, ensuring secure, scalable, and compliant data flows for analytics and AI/ML workloads.

You will mentor teams, define reusable architectural patterns, and collaborate with stakeholders to shape data strategy, oversee data quality, lineage, and

Qualifications

• Strong organizational, communication, and stakeholder management skills, with the ability to lead client workshops, internal architecture discussions, and proposals.
• Strong experience in enterprise data architecture, including conceptual, logical, and physical data modeling for transactional and analytical systems.
• Hands-on experience designing data platforms and pipelines using Apache NiFi and related integration technologies, with knowledge of data formats such as JSON, XML, and Avro, and protocols such as HTTP, TCP, and Kafka.
• Experience designing modern data architectures including data lakes, lakehouses, data warehouses, ETL/ELT frameworks, and batch and streaming ingestion patterns.
• Strong understanding of data governance, metadata management, data lineage, data cataloging, master/reference data management, and data quality practices including profiling, validation, reconciliation, and stewardship.
• Strong knowledge of database design, SQL, query optimization, performance tuning, partitioning, indexing, and scalable cloud data architecture.
• Experience designing secure data architectures with focus on data classification, encryption, masking, access control, retention, privacy, and regulatory compliance.
• Ability to define data strategy, architecture standards, reusable patterns, and non-functional requirements for scalability, availability, observability, reliability, and disaster recovery.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Experience enabling analytics, BI, and AI/ML use cases through trusted, governed, and consumable data design, and ability to coach teams and provide subject matter guidance.

Responsibilities

• Define and drive enterprise data architecture, high-level solution designs, and reusable patterns for data platforms, pipelines, and integration solutions including Apache NiFi and AI-NEXT platform use cases.
• Design conceptual, logical, and physical data models and ensure alignment across source systems, integrations, operational platforms, and analytical environments.
• Architect, optimize, and troubleshoot batch and real-time data pipelines and workflows for performance, scalability, reliability, and cost efficiency.
• Establish and govern data quality, integrity, security, metadata, lineage, cataloging, and master/reference data practices across the data lifecycle.
• Ensure database and platform performance through sound architecture, query optimization, tuning, resilience planning, observability, and disaster recovery considerations.
• Collaborate with product architects, engineering teams, security teams, business stakeholders, and other cross-functional partners to translate requirements into scalable and secure data solutions.
• Support analytics, BI, and AI/ML initiatives by designing trusted, consumable, and governed data assets for downstream use.
• Guide delivery teams on implementation approach, mentor team members, and help shape data strategy, technical direction, and subject matter capability.
• Support pre-sales and solutioning activities by contributing to data architecture assessments, proposals, and client-facing discussions.

Skills

Enterprise data architecture

Data modeling

SQL

Cloud platforms

Data governance

Data integration

ETL/ELT

Apache NiFi

Mulesoft

Kafka

Data warehousing

Education

Bachelor's degree in a relevant field

Tools

Fabric

Mulesoft

IBM Sterling

Apache NiFi

Docker

Kubernetes

Job description

Experienced data architect profile with strong expertise in enterprise data architecture, integration architecture, data modeling, database design, and SQL, with proficiency in at least one cloud platform. Strong understanding of data integration and management tools (Fabric, Mulesoft, IBM Sterling, Apache NiFi etc.), ETL/ELT, data warehousing, big data technologies, metadata management, data governance, master and reference data, and scalable data platforms that support analytics and AI/ML workloads.

Skills Requirements:

• Strong organizational, communication, and stakeholder management skills, with the ability to lead client workshops, internal architecture discussions, and proposals.
• Strong experience in enterprise data architecture, including conceptual, logical, and physical data modeling for transactional and analytical systems.
• Hands-on experience designing data platforms and pipelines using Apache NiFi and related integration technologies, with knowledge of data formats such as JSON, XML, and Avro, and protocols such as HTTP, TCP, and Kafka.
• Experience designing modern data architectures including data lakes, lakehouses, data warehouses, ETL/ELT frameworks, and batch and streaming ingestion patterns.
• Strong understanding of data governance, metadata management, data lineage, data cataloging, master/reference data management, and data quality practices including profiling, validation, reconciliation, and stewardship.
• Strong knowledge of database design, SQL, query optimization, performance tuning, partitioning, indexing, and scalable cloud data architecture.
• Experience designing secure data architectures with focus on data classification, encryption, masking, access control, retention, privacy, and regulatory compliance.
• Ability to define data strategy, architecture standards, reusable patterns, and non-functional requirements for scalability, availability, observability, reliability, and disaster recovery.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Experience enabling analytics, BI, and AI/ML use cases through trusted, governed, and consumable data design, and ability to coach teams and provide subject matter guidance.

Responsibilities:

• Define and drive enterprise data architecture, high-level solution designs, and reusable patterns for data platforms, pipelines, and integration solutions including Apache NiFi and AI-NEXT platform use cases.
• Design conceptual, logical, and physical data models and ensure alignment across source systems, integrations, operational platforms, and analytical environments.
• Architect, optimize, and troubleshoot batch and real-time data pipelines and workflows for performance, scalability, reliability, and cost efficiency.
• Establish and govern data quality, integrity, security, metadata, lineage, cataloging, and master/reference data practices across the data lifecycle.
• Ensure database and platform performance through sound architecture, query optimization, tuning, resilience planning, observability, and disaster recovery considerations.
• Collaborate with product architects, engineering teams, security teams, business stakeholders, and other cross-functional partners to translate requirements into scalable and secure data solutions.
• Support analytics, BI, and AI/ML initiatives by designing trusted, consumable, and governed data assets for downstream use.
• Guide delivery teams on implementation approach, mentor team members, and help shape data strategy, technical direction, and subject matter capability.
• Support pre-sales and solutioning activities by contributing to data architecture assessments, proposals, and client-facing discussions.