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.

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.