Top Tech Jobs & Startup Jobs in Delhi

Lead the design, development, optimization, and support of scalable data engineering and database marketing solutions. Build and maintain ETL/ELT pipelines using Azure Data Factory, Databricks, and related technologies; create technical designs; troubleshoot production issues; conduct code reviews; and ensure performance, quality, and documentation standards. Collaborate with clients and internal stakeholders, manage small project teams, contribute to architecture decisions, and mentor team members.

Develop and maintain Power BI dashboards and reporting solutions, write and optimize SQL queries, build data models and DAX calculations, validate data quality, and translate business requirements into actionable insights. The role involves stakeholder collaboration, troubleshooting reporting issues, documenting dashboard logic and data sources, and ensuring accurate, consistent reporting.

Design, build, and optimize scalable data pipelines and event-driven architectures on GCP. Develop Python applications, integrate external APIs, manage BigQuery and cloud storage solutions, and automate deployments with Cloud Build. Ensure pipeline performance, scalability, cost efficiency, data quality, governance, lineage, and security while collaborating with cross-functional teams and documenting technical solutions.

QA Analyst responsible for validating data warehouse and database deliverables through functional, regression, load, system, and UAT testing. Duties include writing test plans and SQL scripts, creating test data, tracking defects in Jira, validating fixes, investigating data-quality issues, and communicating test status to project teams and clients. The role requires SQL, ETL, data warehousing, cloud, Agile, and stakeholder communication experience.

Build and maintain cloud data pipelines, transformation workflows, orchestration, governance, and access controls for analytics products. Integrate APIs, Databricks, BI tools, and multi-tenant data platforms while troubleshooting quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on platform features, architecture documentation, infrastructure migrations, and production support.

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

Leads quality assurance activities for data warehouse and database projects, including test planning, SQL scripting, functional and regression testing, defect tracking, data quality investigation, and UAT. Translates requirements into comprehensive test cases, validates fixes, communicates test status, establishes QA standards, and mentors 2–3 QA associates. Requires SQL, ETL, cloud, JIRA, Agile, and database testing expertise.

Maintains, cleans, manipulates, and secures operational and analytics data. Designs scalable data pipeline architectures, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports data, product, design, analytics, and data science teams with technical data needs.

Designs and develops scalable cloud data platforms and ETL/ELT pipelines using Python and SQL. Optimizes performance and costs, implements governance, security, quality, and compliance controls, and supports analytics consumption. Collaborates with architects and stakeholders, contributes to pre-sales prototypes and demos, mentors junior developers, conducts code reviews, and recommends improvements across AWS, Azure, and GCP environments.

Designs and implements multi-cloud data architecture solutions across AWS, Azure, and GCP. Responsibilities include data modeling, ETL/ELT pipelines, data warehousing, analytics platforms, governance, security, compliance, cost modeling, architecture proposals, and client presentations. The architect must remain hands-on with Python, SaaS data tools, MDM platforms, and agentic AI; support pre-sales and RFP responses; mentor junior architects; and integrate AI/ML into data architectures.