Top Data & Analytics Jobs in Pune, MH

Build and maintain petabyte-scale data pipelines, entity-centric data models, and distributed workflows using Databricks and Spark. Partner with data science teams to productionize ML features and artifacts, optimize ETL/ELT processes, and implement testing, monitoring, observability, and incident response. Collaborate with governance and operations teams to ensure secure, compliant data management while supporting scalable merchant identity and trust-profile systems.

Develops, tests, validates, and supports large-scale batch and real-time data platforms using Spark, Kafka, and NiFi. Builds resilient data pipelines, validation frameworks, distributed storage solutions, deployment automation, and operational tooling. Optimizes performance, scalability, and reliability; supports production troubleshooting and incident resolution; contributes to architecture, CI/CD, documentation, code reviews, and engineering standards; and mentors junior engineers.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Owns reporting and analytics for Mastercard’s Product Express platform. Translates stakeholder needs into scalable dashboards, metrics, and reporting solutions; monitors adoption and performance; analyzes data; and recommends operational improvements. The role also leads onboarding digitization, prioritizes platform enhancements, and identifies AI applications such as anomaly detection, automated insights, natural-language querying, and proactive recommendations.

Develop, test, validate, and support large-scale batch and real-time data platforms using Spark, Kafka, and NiFi. Build resilient data pipelines, validation frameworks, reusable platform components, and deployment automation. Optimize distributed systems for performance, scalability, and reliability; support production operations, monitoring, troubleshooting, and incident resolution. Collaborate on architecture, CI/CD, documentation, and engineering standards while mentoring junior engineers.

Lead the strategy, architecture, and evolution of enterprise data platforms supporting secure data exchange, discovery, governance, integration, and self-service consumption. Establish scalable patterns for data quality, lineage, observability, privacy, and security while reducing bespoke workflows through automation. Provide technical leadership across engineering organizations, influence architecture decisions, mentor technical teams, and translate complex business needs into reusable cloud and on-premises data solutions.

Build and maintain large-scale backend data systems, batch and streaming pipelines, datamarts, and cloud data workloads. Develop AI-powered agents for anomaly detection and workflow automation, using Scala, Python, Java, Spark, Cloudera, Databricks, and AWS. Support platform reliability, data integrity, downstream analytics, documentation, Agile collaboration, and team mentoring.

Lead design and build cloud-native batch and real-time data platforms. Develop scalable ETL/ELT and streaming pipelines (Spark, Kafka, Flink), ensure data quality, governance, observability, security, cost optimization, and drive platform engineering excellence and team mentorship.

Design, build, and maintain scalable batch and near-real-time data pipelines and curated datasets. Enable ML-ready feature datasets, implement reliability patterns, ensure secure data handling and governance, and drive engineering best practices in collaboration with data science, platform, and BI stakeholders.