Top Tech Jobs & Startup Jobs in Delhi

Build and maintain scalable AWS data platforms and batch or streaming pipelines. Develop SQL transformations and Python ETL workflows using services such as S3, Glue, EMR, Athena, and Redshift. Design data lakes, warehouses, and analytics-optimized models while monitoring performance, reliability, security, and data quality. Troubleshoot pipeline issues, support deployments, and collaborate with architects, DevOps, QA, product teams, and business stakeholders.

Design and deliver scalable AWS data platforms, including cloud data lakes, warehouses, batch and streaming pipelines, and SQL- and Python-based transformations. Build reliable, fault-tolerant data workflows using AWS services, Spark, and PySpark. Own solutions from development through production support, including performance tuning, cost optimization, troubleshooting, security, and compliance. Collaborate with architects, clients, product owners, DevOps, QA, and global stakeholders while contributing to technical design reviews and engineering best practices.

Lead the architecture and implementation of Salesforce Data Cloud solutions, including data models, ingestion pipelines, identity resolution, activation workflows, and MarTech integrations. Collaborate with marketing, analytics, engineering, and business stakeholders to deliver personalized customer experiences. Ensure data quality, privacy compliance, performance, and troubleshooting across platforms. Mentor engineers, guide technical standards, support segmentation and journey orchestration, and contribute to roadmap planning.

Leads QA activities for data warehouse and database-focused software projects. Defines testing scope, creates test plans and SQL scripts, designs comprehensive test cases, performs functional, regression, load, system, and UAT testing, manages defects through resolution, investigates data quality issues, communicates status to stakeholders, establishes QA best practices, and mentors 2–3 QA associates.

Designs and leads enterprise-scale cloud data platforms and analytics solutions using Snowflake, Python, SQL, and Azure technologies. Responsibilities include data architecture, migration, governance, optimization, and technical leadership. The role also architects generative AI, LLM, and RAG solutions, mentors engineering teams, conducts design reviews, develops roadmaps, and translates business requirements into secure, scalable technical solutions.

Designs and maintains scalable data engineering solutions using Python, SQL, and Snowflake. Builds ETL/ELT pipelines, data warehouses, data lakes, and React interfaces while integrating generative AI, LLMs, RAG, and intelligent automation. Responsibilities include Snowflake architecture, data governance, CI/CD, testing, performance optimization, stakeholder collaboration, Agile participation, business data analysis, and mentoring junior developers.

Develops and maintains automated frontend and backend test suites for the Pipes advertising QA platform. Responsibilities include E2E testing, test coverage monitoring, CI/CD pipeline execution, diagnosing flaky tests, improving testability, documenting defects, and collaborating with developers and product managers across three agile engineering squads.

Design, build, and optimize scalable ETL/ELT pipelines and event-driven data architectures on GCP. Develop Python applications, integrate external APIs, deploy cloud services, automate CI/CD with Cloud Build, and manage data quality, governance, lineage, security, performance, and cost efficiency. Collaborate with cross-functional teams to deliver scalable data solutions and maintain technical documentation.

Build and maintain cloud data pipelines, transformations, orchestration workflows, governance controls, and analytics-ready datasets. Integrate APIs, Databricks, BI tools, and platform connectors while troubleshooting data quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on architecture, access management, infrastructure migrations, documentation, and production support.

Provides technical guidance across the software development lifecycle, including requirements analysis, design, development, testing, documentation, release processes, and feature delivery. Owns complex application features, resolves technical issues, improves code stability and reusability, integrates technologies, estimates work, identifies security and usability issues, delivers training, and mentors software engineers.