Lead Developer

Cognizant · IT Consulting & Digital Transformation · IT Outsourcing & Dedicated Teams · SQL · Azure Data Factory · Dimensional Modeling · 3 hours ago
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Salary
≈ $15k – $37k per year (Estimated)
Location
Hybrid (Chennai, India)
Seniority
Staff
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Oct 28, 2026. Cognizant scores A on the Alion truth index.

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Cognizant

Cognizant is an American information technology services and consulting company founded in 1994 as an in-house technology unit of Dun and Bradstreet in Chennai, India, and headquartered in Teaneck, New Jersey. It builds, integrates and operates software and infrastructure for large enterprises, with particularly deep positions in healthcare, financial services, insurance and life sciences, delivered by a global workforce of more than three hundred thousand people concentrated in India. Listed on Nasdaq, the company competes with the large Indian and global systems integrators and has reoriented its offerings around cloud migration, data platforms and AI-assisted engineering.
cognizant.com · HQ: Teaneck, United States · Cloud Consulting & Migration · Engineering Services · IT Outsourcing & Dedicated Teams · IT Consulting & Digital Transformation · Systems Integrators · 5000+ employees · Est. 1994
Tech stack of Cognizant
Backed by Peak XV Partners

Job Summary

Serve as a senior technical expert acting as a lead developer in data engineering initiatives that use SQL Azure Databricks Azure Data Lake Store and Azure Data Factory to build scalable data solutions in a hybrid work model. Contribute to high quality data platforms that support investment banking focused analytics and operational reporting in a collaborative day shift environment.

Responsibilities

  • Design robust data engineering solutions using SQL Azure Databricks Azure Data Lake Store and Azure Data Factory to deliver secure and scalable data pipelines for complex analytics needs in investment banking contexts
  • Develop efficient batch and near real time data pipelines that ingest transform and curate structured and semi structured data to enable high quality downstream reporting and regulatory analysis
  • Implement optimized SQL queries stored procedures and data models that improve query performance reduce compute cost and ensure reliable data access for analytical users and operational teams
  • Configure and maintain Azure Databricks clusters notebooks and jobs to orchestrate data transformations while managing resource utilization and promoting consistent engineering practices
  • Build and manage Azure Data Factory pipelines triggers and activities to automate data movement across diverse sources and sinks while enforcing standardized patterns and reusable components
  • Administer Azure Data Lake Store structures access patterns and data lifecycle policies to ensure organized storage cost effectiveness and secure handling of sensitive financial information
  • Coordinate with business analysts and stakeholders from investment banking operations to translate requirements into precise technical specifications and data transformations that align with business objectives
  • Collaborate with quality engineering and operations teams to design validation checks reconciliation controls and monitoring processes that ensure data completeness accuracy and timeliness
  • Guide peers on best practices in coding testing documentation and version control to maintain high engineering standards and consistent delivery of maintainable data solutions
  • Perform root cause analysis for data issues production incidents and performance bottlenecks then implement durable fixes and preventive improvements that enhance platform stability
  • Document solution designs data flow diagrams data dictionaries and operational runbooks in clear language so that cross functional teams can easily understand maintain and extend the platforms
  • Engage with architecture and security teams to align implementations with enterprise reference patterns compliance expectations and resiliency objectives across the Azure data platform
  • Participate in sprint planning backlog refinement and technical reviews to ensure that stories are scoped realistically risks are identified early and deliverables meet quality expectations for stakeholders

    • Qualifications

  • Combine extensive hands on experience in SQL Azure Databricks Azure Data Lake Store and Azure Data Factory with an ability to design end to end data pipelines that support analytics for financial services
  • Apply knowledge of investment banking operations or investment banking and brokerage processes when available to shape data models transformation rules and controls that reflect real world trade and post trade workflows
  • Demonstrate strong understanding of data warehousing concepts dimensional modeling and performance tuning methods that support high volume and low latency analytical workloads
  • Utilize experience with hybrid working environments and cross functional collaboration tools to maintain clear communication effective documentation and timely delivery across distributed teams
  • Exhibit solid problem solving skills attention to detail and a continuous improvement mindset that support reliable production operations and incremental enhancement of the data platform
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