Top Python Developer Jobs in Delhi

As a Senior Data Scientist, you will utilize analytical methods and programming skills to derive insights from data, design solutions, and communicate effectively with stakeholders.

The Senior Data Analytics Engineer at MariaDB designs and builds data pipelines, writes SQL queries, develops internal tools, and translates data into actionable insights. They will collaborate closely with stakeholders and leverage AI tools to enhance analytics workflows.

The Revenue Operations Analyst will manage revenue analytics for publisher accounts, create reports, identify growth opportunities, and collaborate with teams to optimize monetization strategies.

The Senior Pricing and Paywalls Product Analyst will automate analytical data collection, analyze product metrics, organize A/B tests, and support the generation of product hypotheses to identify growth opportunities.

The Senior Data Scientist will develop strategies for analytics, lead operational processes, and promote the use of data science solutions while collaborating internally and externally to ensure effective adoption.

Design, build, and maintain scalable, secure multi-cloud solutions. Lead cloud migrations, implement infrastructure-as-code and CI/CD pipelines, automate provisioning, and advise clients on architecture, performance, security, and third-party technology selection.

Designs, plans, and drives complex, large-scale multi-vendor network implementations and upgrades. Leads troubleshooting, builds test lab simulations, creates configurations and run books, coordinates vendors, and mentors engineering teams. Supports escalations, automations, and quarterly maintenance events while ensuring minimal service impact and aligning network initiatives to business needs.

Lead design, planning, and implementation of complex, multi-vendor, international network projects. Troubleshoot high‑complexity incidents, build test lab simulations, drive upgrades/migrations, produce configs/runbooks, mentor teams, and coordinate vendors for circuit delivery and problem resolution.

Senior backend-focused engineer responsible for designing and delivering features in Go and React, building scalable distributed architectures, mentoring teammates, coordinating with product and business stakeholders, instrumenting production systems, and leading small teams to ship high-quality APIs and services.

Lead reliability, monitoring, and production support for cloud-based SaaS platforms. Implement observability, SLO/SLI frameworks, CI/CD and IaC automation, container orchestration, and security best practices. Drive incident management, RCA, disaster recovery, capacity planning, and cross-team collaboration to improve uptime, scalability, and cost efficiency.

Design and implement test strategies for 4G/5G core products, develop tools for verification, and ensure high-quality releases.

Design and implement web scraping solutions using languages like Python or JavaScript and libraries such as Beautiful Soup, Selenium, and Scrapy. Automate data collection, perform cleansing and validation, transform data for reporting and integration, document processes, collaborate with cross-functional teams, and transfer knowledge to stakeholders.

Design, deploy, and optimize cloud-native microservices platforms. Build and maintain CI/CD pipelines, manage containerized applications with Docker/Kubernetes, implement IaC (Terraform), and configure observability stacks including Prometheus, Grafana, ELK/EFK, and tracing tools.

Enable SRE teams to deliver end-to-end business observability with Dynatrace: integrate custom business metrics via Python, design business-centric dashboards, apply AI-based root cause analysis, monitor SAP BTP and Kubernetes, and coach SREs. Deliver optimized Dynatrace setups, production-ready scripts, dashboards, alerting rules, and documentation.

Build and operate reliable, secure, and scalable CI/CD platforms, cloud infrastructure, and observability solutions. Automate deployments, manage Kubernetes and Jenkins pipelines, integrate build tools, and use monitoring/incident tools to support large-scale systems.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.

Design, develop, and maintain Snowflake-based data solutions and ETL/ELT pipelines from S4 HANA. Build complex SQL, Python scripts, stored procedures and UDFs, apply Snowflake data modeling and performance best practices, work with Snowflake containers and SNP Glue, support reporting (Power BI), and engage in project documentation and tracking.

Design and deliver enterprise AI solutions on SAP BTP: build/deploy ML models (AI Core/Launchpad), integrate Joule across SAP products, prototype AI use cases, create intelligent workflows with SAP Build Process Automation, define AI architecture, MLOps pipelines, and multi-cloud integration patterns aligned with Clean Core.

Design and implement web scraping solutions on SAP BTP, automate data collection, cleanse and transform data for reporting and integration, document processes, and transfer knowledge to stakeholders.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Build an AI-native EHR: design, implement, and ship end-to-end features across frontend, backend, data, and infra. Use LLMs and AI tooling, maintain reliability, refactor code, and iterate with users in a fast-moving startup environment.

Design, deploy, and automate scalable hybrid cloud infrastructure across AWS and Azure using IaC (Terraform/CloudFormation/ARM/Bicep). Build CI/CD pipelines, implement observability (Prometheus, Grafana, Datadog, CloudWatch, Splunk), lead incident response and RCA, define SLIs/SLOs, enforce DevSecOps practices, manage access (IAM/Azure AD/RBAC), and collaborate with engineering teams to optimize reliability and performance.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

The Platform Engineer will design and maintain testing frameworks using Playwright, ensuring quality and performance in a scalable platform through automation and collaboration with teams.

As an Integration Engineer at Drivetrain, you will manage customer integrations from kick-off to go-live, troubleshoot data issues, build connectors, and advocate for customers while providing technical guidance.