Top Data Engineer Jobs in Mumbai

Designs and maintains scalable AWS, Snowflake, and dbt data pipelines and transformation frameworks. Builds ETL/ELT workflows, dimensional models, data quality frameworks, and optimized SQL workloads for structured and semi-structured data. Responsibilities include pipeline orchestration, monitoring, troubleshooting, CI/CD, legacy ETL modernization, documentation, production support, and collaboration with technical teams. The role also contributes to architecture discussions, Agile delivery, code reviews, and mentoring junior engineers.

Designs, develops, and supports enterprise ETL/ELT pipelines, data warehouses, and integrations using SQL Server, SSIS, and Azure Data Factory. Responsibilities include modernizing legacy SSIS workloads, optimizing SQL performance, integrating APIs and SFTP, ensuring data quality, troubleshooting production issues, implementing CI/CD, and supporting hybrid cloud environments. The role also contributes to architecture decisions and may incorporate AI-enabled data engineering, anomaly detection, and intelligent automation.

Build and support large-scale, real-time kdb+ analytics platforms processing billions of daily messages. Develop kdb Insights or KDB-X modules, packages, pipelines, performant q queries, and functions for trading desks. Create SRE tools to detect performance bottlenecks across q, Python, and Java processes running continuously in AWS. Support ingestion stability, Kubernetes deployments, PyKX data processing, and cloud-native analytics infrastructure.

Build and operate production data platforms, tooling, and infrastructure supporting autonomous pipeline delivery. Own monitoring, alerting, DataOps, Kafka, Kubernetes, Terraform, AWS, Databricks/Spark, dbt, and Airflow environments. Improve platform reliability, troubleshoot production issues, and partner with product, analytics, and infrastructure teams. Champion AI adoption and automation while enabling high-throughput, low-latency data processing and strong developer experience.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Designs, builds, and maintains batch and real-time data pipelines, cloud-native ETL/ELT systems, and lakehouse solutions for client data and AI projects. Integrates diverse data sources, applies data quality and observability practices, and prepares modeled data for analytics, machine learning, GenAI, feature stores, and vector databases. Uses SQL, Python, cloud platforms, modern data tools, Git, and CI/CD. The role also involves client-facing consulting, independent delivery, technical collaboration, and improving engineering standards.

Build and lead end-to-end data engineering solutions, including ETL pipelines, data staging, architecture, application interfaces, and cloud-based data platforms. Partner with business, operations, and infrastructure teams to deliver secure, scalable, high-quality solutions. Drive engineering standards, technical improvements, agile delivery, compliance, and continuous improvement across analytics and data warehousing environments.

Develop, validate, deploy, and monitor machine learning models for enterprise business and operational use cases. Responsibilities include data preparation, feature engineering, model development, explainability, ML pipeline construction, model versioning, retraining, monitoring, workflow integration, documentation, and knowledge transfer. The role requires advanced Python, Azure Machine Learning, SQL, production ML Ops, and experience delivering end-to-end solutions onsite in Abu Dhabi.

The description indicates a payroll-focused analyst role within the Payroll team, but provides no specific responsibilities, qualifications, technologies, salary, travel, or clearance requirements.

Designs, develops, tests, and maintains scalable ETL pipelines using Azure services and streaming technologies. Collaborates with architects, analysts, scientists, and business stakeholders to ensure data accuracy and integrity. Builds data models and documentation, optimizes workflows, monitors production data flows, troubleshoots issues, implements data-quality controls, and integrates cloud and on-premise storage while supporting governance, security, and privacy standards.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain data pipelines using Databricks, Python, SQL, and Spark. Apply data modeling, cloud data services, version control, and CI/CD practices. Support streaming pipelines, workflow orchestration, data governance, security controls, and downstream BI requirements. Experience with AWS or Azure is required, while Databricks certification, streaming technologies, orchestration tools, and Power BI exposure are preferred.

Build and optimize cloud-scale data infrastructure processing millions or billions of events. Responsibilities include cloud migration, data pipeline and workflow development, data modeling, analytics systems, troubleshooting, query development, and performance recommendations. The role uses GCP, Spark, Kafka, Dataflow, Dataproc, Airflow, databases, and Python, Java, or Scala, while collaborating with teams and mentoring junior engineers.

Provides technical leadership for enterprise data engineering, designing scalable batch and real-time data platforms, pipelines, and frameworks. Builds cloud-native AWS solutions using modern data engineering tools, while establishing governance, metadata, lineage, quality, security, and compliance practices. Enables analytics, reporting, machine learning, and generative AI initiatives through trusted data foundations. Collaborates with engineering teams, analysts, stakeholders, and architects to deliver reliable data solutions.

Build and lead enterprise data platforms and pipelines using Snowflake, AWS, dbt, PySpark, SQL, Teradata, Oracle, and ETL tools. Responsibilities include data transformation, ingestion, cleansing, aggregation, testing, performance optimization, troubleshooting, documentation, production support, and ETL strategy. The role also involves creating technology roadmaps, collaborating with business and infrastructure teams, improving delivery processes, mentoring engineers, and ensuring data quality and regulatory compliance.

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.

Lead data engineering delivery on the Databricks Platform, building scalable batch, micro-batch, and streaming pipelines. Design lakehouse solutions using Delta Lake, Iceberg, Delta Live Tables, Unity Catalog, Spark, and cloud data platforms. Develop workflow orchestration, data quality, observability, CI/CD, and infrastructure automation solutions. Collaborate with architects and cross-functional teams, contribute to Databricks Center of Excellence initiatives, and deliver solution accelerators for clients.

Lead the design and delivery of scalable data pipelines, models, quality frameworks, and cloud-based data solutions. Partner with analysts, architects, and business stakeholders to define technical architecture, evaluate emerging technologies, troubleshoot data ecosystem issues, and advance engineering practices. Mentor data engineers, lead project teams, contribute to shared data libraries, and support hiring and onboarding. The role requires strong expertise in Google Cloud, Python, SQL, BigQuery, Dataflow, Composer, dbt, observability, orchestration, and modern data engineering.

Designs, develops, and optimizes scalable data pipelines, platforms, and models using Snowflake, dbt, Azure, and Databricks. Integrates and harmonizes multiple data sources, establishes data-quality and governance practices, applies security controls, and enables analytics and reporting. The role also translates stakeholder requirements into sustainable data solutions, optimizes performance and cost, monitors workflows, and contributes to engineering standards and continuous improvement.

Provides technical sales support for data and AI solutions in upstream oil and gas. Identifies customer needs, develops technical solutions, builds proofs of concept, supports demonstrations and technology launches, manages technical issues, and contributes to sales strategies and revenue targets. The role requires expertise in exploration and production workflows, data science, AI, analytics, and client relationship management.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Designs and maintains scalable data pipelines, warehouses, lakehouses, semantic models, and Fabric architecture. Manages data governance, security, capacity optimization, privacy compliance, and large-scale processing. Provides technical direction, mentors junior engineers, collaborates with BI developers, and documents engineering standards and procedures.

Build and maintain Microsoft Fabric data pipelines, Lakehouse and Warehouse solutions, and Power BI semantic models, dashboards, and reports. Develop ETL/ELT processes, dimensional models, DAX measures, paginated reports, data-quality checks, and performance optimizations. Support production reporting, user acceptance testing, refresh automation, and deployment through Git, Azure DevOps, and Fabric pipelines. Collaborate with business analysts, data engineers, and stakeholders to deliver reliable reporting solutions.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.