Architect

Cognizant; Kolkata District; Hybrid; INR 3,500,000 - 7,000,000; Full time

Job summary

Cognizant is seeking an experienced Data & Analytics Architect to lead the design of scalable Databricks-based solutions for insurance-focused domains. You will architect data pipelines, optimize SQL models, and drive governance across hybrid delivery environments.

The role requires deep PySpark expertise, strong collaboration with product and business stakeholders, and a track record of delivering enterprise-grade data platforms with long-term resilience.

Qualifications

• Extensive experience in Databricks SQL to design analytical models, reporting layers, and interactive queries.
• Proficient in Databricks Workflows to orchestrate jobs, dependencies, and automated data ops.
• Deep PySpark expertise to implement scalable transformations and enforce data quality across pipelines.
• Knowledge of life and annuities insurance to shape data models and validations when required.
• Demonstrated 12–16 years of data engineering/architecture experience on complex enterprise solutions.
• Strong communication and collaboration skills in a hybrid, non-travel environment.
• Experience integrating Databricks solutions into broader enterprise architectures for long-term value.

Responsibilities

• Design robust data architecture on Databricks platforms aligned with enterprise standards.
• Develop end-to-end data pipelines using PySpark and Databricks Workflows.
• Optimize Databricks SQL queries and data models for performance and cost.
• Create reusable frameworks for ingestion, transformation, and data quality.
• Collaborate with product and business stakeholders to define data architecture.
• Integrate data from diverse sources into unified models for actuarial and financial use.
• Define standards for coding, testing, and documentation of PySpark jobs and Databricks assets.
• Implement monitoring and alerting on Databricks Workflows for reliable data delivery.
• Partner with security and compliance teams to embed governance and privacy controls.
• Guide teams on Databricks cluster usage, storage, and caching for performance vs cost.
• Document decisions, data flows, and dependencies for future enhancements.
• Coordinate with cross-functional teams in a hybrid model to plan releases.

Skills

Databricks SQL

Databricks Workflows

PySpark

Data architecture

Data quality

Communication

Tools

Databricks Platform

SQL Queries

Job description

Job Summary

Serve as an architect for data and analytics solutions built on Databricks platforms designing scalable pipelines and frameworks that leverage Databricks SQL Databricks Workflows and PySpark. Apply extensive experience to optimize hybrid day shift delivery enable reliable insights for stakeholders and support innovation in insurance focused business environments.

Responsibilities

• Design robust data architecture on Databricks platforms that aligns with enterprise standards and enables secure scalable analytics across hybrid work environments
• Develop end to end data pipelines using PySpark and Databricks Workflows that transform complex raw data into curated datasets ready for business consumption
• Optimize Databricks SQL queries and data models to improve performance reduce compute cost and ensure consistent response times for analytical workloads
• Create reusable frameworks and patterns for ingestion transformation and data quality that can be adopted by engineering teams across multiple initiatives
• Collaborate with product and business stakeholders to translate analytical and reporting needs into well defined data architecture and implementation plans
• Integrate data from diverse source systems into unified models that support actuarial analysis financial reporting and operational monitoring for insurance focused solutions
• Define standards for coding testing and documentation of PySpark jobs and Databricks assets to promote maintainability and long term platform resilience
• Implement monitoring and alerting strategies on Databricks Workflows to ensure timely detection of pipeline failures and to support reliable data delivery in day shift operations
• Partner with security and compliance teams to embed data governance privacy controls and audit readiness into all Databricks based solutions
• Guide teams on efficient use of Databricks clusters storage and caching options to balance performance objectives with infrastructure cost management
• Document architectural decisions data flows and dependency maps so that teams can easily understand solution design and support future enhancements
• Coordinate with cross functional teams in a hybrid work model to plan releases manage dependencies and ensure smooth deployment of new data capabilities

Qualifications

• Apply extensive experience in Databricks SQL to design analytical models reporting layers and interactive queries that support complex business insights
• Leverage advanced proficiency in Databricks Workflows to orchestrate jobs manage dependencies and automate data operations with strong reliability
• Utilize deep PySpark expertise to implement scalable transformations handle large data volumes and enforce data quality rules across the pipeline lifecycle
• Draw on knowledge of life and annuities insurance to shape data models metrics and validations that reflect key policy claim and risk concepts when required
• Demonstrate proven ability gained from twelve to sixteen years of experience in data engineering and architecture to handle complex enterprise grade solutions
• Apply strong communication and collaboration capabilities to work effectively with business and technology partners in a hybrid non travel environment
• Use experience with modern data platforms and cloud ecosystems to integrate Databricks solutions into broader enterprise architectures for long term value

Certifications Required

Preferred certifications include Databricks Certified Data Engineer Professional or Databricks Certified Data Engineer Associate.