Top Data Engineer Jobs in Delhi

Build and scale an AI-powered stock research platform: design data pipelines ingesting SEC and market data, develop backend APIs, implement semantic search with embeddings and vector DBs, improve production reliability and performance, and own features end-to-end from design through deployment and monitoring.

Build and operate automated data quality checks, monitoring, alerting, and dashboards; integrate quality into data pipelines and governance tooling; automate anomaly detection and support auditors, analysts, and stewards to surface issues in near-real-time.

The Big Data Engineer will design and develop scalable data solutions, build data pipelines, ensure data quality, and collaborate across teams.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

Responsible for guiding teams in utilizing technology effectively, mentoring colleagues and devising advanced solutions to meet client challenges.

As a Senior Engineer in Data Analytics, you'll work on challenging problems using cutting-edge technology alongside talented colleagues to drive innovative solutions.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

Design, build, and scale cloud-native Python backend services and data-backed applications. Develop RESTful APIs, microservices, and asynchronous event-driven systems. Deploy and operate production workloads on Kubernetes and at least one public cloud (AWS or Azure). Work with cloud data warehouses (Snowflake or Databricks) and relational databases, ensuring scalable, reliable distributed systems.

Design, operate, and secure scalable batch and streaming data pipelines and platforms. Own ingestion, transformation, serving, data quality, and access controls. Partner with product, risk, compliance, and InfoSec to ensure production reliability, correctness, and audit readiness for financial data.

Design, build, and optimize scalable data pipelines and models using Spark, Kafka, Airflow, dbt, and SQL across GCP and AWS. Ingest and normalize marketing and analytics data, ensure data quality and governance, support analytics and reporting, and collaborate with cross-functional global teams.

The role involves building and scaling the Acceldata Open Data Platform, focusing on observability, coding, debugging, and collaborating with teams and the open-source community.

The Snowflake Senior Data Engineer will lead projects in data engineering, utilizing Snowflake and various AWS services to optimize data workflows and storage, while also guiding the team and collaborating with stakeholders.

The Principal Data Science Engineer will leverage statistical analysis and machine learning to optimize airline revenue performance and customer experience, collaborating with teams to develop data models and algorithms, and support product development cycles.

Own end-to-end design and delivery of IoT and real-time data platform features: define architecture, build Node.js backends and React/Vue dashboards, integrate device telemetry (MQTT/WebSockets), manage MongoDB and time-series data, ensure CI/CD and cloud deployments, and mentor junior developers.

As a Principal Engineer for Data Infrastructure, you'll oversee our databases and data warehousing, optimize performance, and lead technology integrations to enhance data processing capabilities.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Design, build, and maintain Power BI dashboards and semantic data models using Power BI and Microsoft Fabric. Optimize performance, troubleshoot dataset and refresh issues, ensure governance and data accuracy, and collaborate with stakeholders to modernize analytics platforms.

Design and scale end-to-end data architecture and real-time/batch pipelines to unify product telemetry, CRM, and billing into a single source of truth. Build data foundations for ML (feature pipelines, training datasets, low-latency scoring), define telemetry/event contracts, implement data quality, monitoring, governance, and enable self-service analytics.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Design, build, and operate batch and real-time data ingestion and transformation pipelines. Develop platform components (storage, compute, pipelines), monitoring, alerting, and data-quality tooling. Diagnose production issues, participate in on-call rotations, and build AI-powered agents to automate engineering and operational tasks.

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 role involves building a centralized data lake on GCP, developing SPARK-powered data pipelines, ensuring data quality, and collaborating with cross-functional teams for advanced analytics and data models.