Inteliment Technologies - Technical Architect - Data Engineering

Inteliment Technologies; Maharashtra; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Inteliment Technologies in Maharashtra is seeking an experienced Enterprise Data Architect to design end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization. You will architect scalable cloud-native data platforms for analytics workloads and define reusable standards.

The role focuses on enabling high-performance, secure analytics environments, building large ETL/ELT pipelines, and guiding cross-functional teams through data architecture

Qualifications

• Experience designing end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization.
• Architect scalable cloud-native data platforms for enterprise analytics workloads.
• Define reusable architecture standards, frameworks, and best practices.
• Enable high-performance, secure, and resilient analytics environments.
• Build and optimize large-scale ETL/ELT pipelines.
• Design modern data lake, Lakehouse, and warehouse architectures.
• Work across structured, semi-structured, and unstructured datasets.
• Improve data reliability, observability, scalability, and performance.

Responsibilities

• Enterprise Data Architecture: design end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization.
• Data Engineering & Platform Modernization: build large-scale ETL/ELT pipelines and modern data lake architectures.
• Decision Intelligence & Visualization: architect executive-grade BI and visualization ecosystems.
• Technical Leadership: guide engineers and analytics teams across architecture and delivery.
• Enterprise Engagement: participate in client workshops and contribute to technical strategy.

Skills

Data Engineering Architecture

Enterprise Analytics Engineering

Data Platform Modernization

Visualization & BI Architecture

Cloud Data Ecosystems

Performance Optimization & Scalability

Python / PySpark / SQL

Spark & distributed data processing

Snowflake / Redshift / BigQuery

AWS / Azure / GCP

ETL/ELT platforms (Informatica, Talend

Power BI / Tableau / Looker

Data lakes / Lakehouse architectures

Tools

Git, CI/CD pipelines

API integrations

Job description

• Design end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization.


• Architect scalable cloud-native data platforms for enterprise analytics workloads.


• Define reusable architecture standards, frameworks, and best practices.


• Enable high-performance, secure, and resilient analytics environments.


• Build and optimize large-scale ETL/ELT pipelines.


• Design modern data lake, Lakehouse, and warehouse architectures.


• Work across structured, semi-structured, and unstructured datasets.


• Improve data reliability, observability, scalability, and performance.

Role & Responsibilities

Enterprise Data Architecture

• Design end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization.


• Architect scalable cloud-native data platforms for enterprise analytics workloads.


• Define reusable architecture standards, frameworks, and best practices.


• Enable high-performance, secure, and resilient analytics environments.


• Build and optimize large-scale ETL/ELT pipelines.


• Design modern data lake, Lakehouse, and warehouse architectures.


• Work across structured, semi-structured, and unstructured datasets.


• Improve data reliability, observability, scalability, and performance.

Data Engineering & Platform Modernization

• Build and optimize large-scale ETL/ELT pipelines.


• Design modern data lake, Lakehouse, and warehouse architectures.


• Work across structured, semi-structured, and unstructured datasets.


• Improve data reliability, observability, scalability, and performance.

Decision Intelligence & Visualization

• Architect executive-grade BI and visualization ecosystems.


• Architect and oversee implementation of AI/ML based Agentic solutions, Gen AI & RAG based systems.


• Build scalable semantic and analytical data models.


• Enable intuitive, business-centric reporting experiences.


• Translate enterprise KPIs and operational requirements into actionable intelligence systems.

Technical Leadership

• Guide engineers, BI developers, and analytics teams across architecture and delivery.


• Drive engineering quality through reviews, governance, and technical standards.


• Mentor teams on scalable design thinking and modern analytics engineering practices.


• Collaborate with delivery, business, and leadership stakeholders to align technology with enterprise goals.

Enterprise Engagement

• Participate in client workshops, architecture discussions, and transformation roadmaps.


• Contribute to solution proposals, estimations, and technical strategy.


• Act as a trusted advisor for enterprise analytics modernization initiatives.

Preferred Candidate Profile

Primary Technical Capabilities

• Data Engineering Architecture and frameworks.


• Enterprise Analytics Engineering.


• Data Platform Modernization.


• Visualization & BI Architecture.


• Cloud Data Ecosystems.


• Performance Optimization & Scalability.

Platform & Technology Ecosystem

• Python / PySpark / SQL.


• Spark & distributed data processing frameworks.


• Snowflake / Redshift / Big Query / modern cloud warehouses.


• AWS / Azure / GCP ecosystems.


• ETL/ELT platforms such as Informatica, Talend, SSIS, or equivalent.


• Power BI / Tableau / Qlik / Looker.


• Data lakes, Lakehouse architectures, and enterprise warehouses.


• Git, CI/CD pipelines, API integrations.

Data & Decision Intelligence Exposure

• Enterprise data modelling (Star/Snowflake schemas).


• Real-time and operational analytics.


• Decision-support systems.


• KPI engineering and business intelligence frameworks.


• Governance, data quality, and analytics reliability.

Collaboration & Business Capabilities

• Enterprise stakeholder communication.


• Solution consulting and architecture presentations.


• Cross-functional collaboration.


• Delivery ownership and execution alignment.


• Agile and iterative engineering practices.