Top Data Engineer Jobs in Mumbai

Design and build scalable cloud-based data solutions, develop ETL pipelines for warehouses and data lakes, implement data quality and validation processes, optimize data storage and processing, and collaborate with data teams, product owners, modelers, analysts, and stakeholders. The role requires expertise in Python, PySpark, SQL, Databricks, AWS services, data modeling, medallion architecture, and pipeline scheduling.

Design and build scalable cloud-based data solutions, develop ETL pipelines for warehouses and data lakes, implement data quality controls, optimize processing and storage, and collaborate with data teams and stakeholders. The role requires Python, PySpark, SQL, Databricks, AWS services, data modeling, scheduling technologies, and analytics expertise.

Designs, develops, and supports scalable cloud data pipelines, transformations, data models, and reporting datasets. Partners with engineering, business intelligence, and business teams to deliver reliable, governed data solutions. Monitors and troubleshoots production processes, improves performance and reliability, contributes to data quality and documentation standards, and participates in code reviews and engineering best practices.

Build and maintain enterprise data pipelines, ELT processes, data models, reporting structures, and analytics solutions. Cleanse, deduplicate, normalize, and govern data across warehouses and data lakes, including Snowflake. Develop dashboards and business intelligence models, ensure data quality and compliance, apply business logic, and collaborate with technical and non-technical stakeholders to deliver accurate insights and improve operational performance.

Designs, develops, and maintains data foundations, pipelines, integrations, and ETL/ELT solutions. Ensures data quality, supports data migration across systems, models and processes data, and delivers scalable solutions using cloud and analytics technologies. Collaborates with technical and business stakeholders, leads implementation initiatives, manages incidents and change processes, presents findings, and mentors junior engineers.

Designs, develops, and maintains data foundations, pipelines, and end-to-end data solutions. Implements ETL and ELT processes, data integration, ingestion, modeling, processing, and quality initiatives across cloud systems. Partners with business and technical stakeholders, mentors junior engineers, supports change and incident management, presents findings, and drives efficient implementation of data strategy and foundation initiatives.

Develops and leads Oracle PL/SQL and Informatica PowerCenter data engineering solutions. Responsibilities include designing ETL workflows, optimizing SQL and data processing, developing data pipelines, supporting data warehouses, reviewing code, resolving production issues, defining technical standards, and collaborating with architects and stakeholders. The role also includes testing, risk assessment, release support, data quality management, documentation, and mentoring while ensuring adherence to data governance and audit standards.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, optimize pipeline performance and scalability, support data migration across systems, and collaborate with analysts and stakeholders to meet data requirements. The role also involves data modeling, database design, and contributing to data strategy and technical problem-solving.

Leads the architecture, delivery, modernization, and operations of enterprise data platforms, warehouses, databases, and engineering solutions. Designs ETL/ELT and CDC pipelines, manages cloud and database environments, improves reliability, security, scalability, and cost efficiency, and drives migrations and strategic initiatives. Provides technical leadership, mentors engineers, coordinates cross-functional projects, manages incidents and root-cause analysis, supports capacity planning, and collaborates with security, cloud, SRE, analytics, and business teams.

Build and manage data connections, validate and normalize incoming portfolio files, automate monitoring and reporting, improve auto-reconciliation via data mining and transformations, support operations and development requests, and communicate technical solutions to non-technical stakeholders. Participate in light on-call duties.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, monitor, and troubleshoot Airflow workflows while supporting migrations from Airflow 2 to 3, Confluent region migration, and the move to Iceberg. Ensure data quality and reliability, apply Snowflake data modeling, and collaborate with data platform teams on modernization initiatives.

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.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, support data integration and migration across systems, and apply data modeling, governance, and database design practices. Collaborate with multiple teams, make technical decisions, mentor junior engineers, and improve data processes. The role requires expertise in Snowflake and leadership as a data engineering subject matter expert.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using Snowflake. Ensures data quality, supports data migration across systems, applies data modeling and governance practices, and collaborates with multiple teams. The role serves as a subject matter expert, makes team decisions, solves cross-team data problems, and mentors junior data engineers.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, support data migration across systems, and apply data modeling, querying, and governance practices. Collaborate with multiple teams, make technical decisions, provide solutions, and mentor junior data engineers. The role requires at least five years of Snowflake experience and 15 years of full-time education.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, support data modeling and governance, and collaborate with multiple teams on data requirements and technical decisions. Serve as a subject matter expert, manage team activities, mentor junior engineers, and provide solutions to cross-team data engineering challenges.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, data governance, and effective integration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, mentor junior engineers, and improve data processes. The role requires expertise in data modeling, database design, cloud-based data processing, and Snowflake data warehousing.

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.

Leads software engineering initiatives, designing, developing, testing, debugging, and stabilizing secure production applications. Reviews code, drives responsible adoption of AI-assisted development tools, improves automation and remediation processes, and evaluates vendor and architectural solutions. Requires advanced programming expertise, full SDLC knowledge, agile and CI/CD experience, application resiliency and security understanding, financial services technology knowledge, and practical cloud-native experience.

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.

Build and support scalable batch and streaming data pipelines, cloud-native data platforms, data lakes, warehouses, and real-time processing systems. Own data initiatives from solution design through deployment, monitoring, performance tuning, and production support. Develop ETL/ELT pipelines, data models, and integration frameworks while collaborating with product, analytics, platform, and DevOps teams. Troubleshoot production issues, improve reliability, and contribute to documentation and engineering best practices.

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.

Develop and support scalable data operations, ETL pipelines, SQL integrations, Azure services, Power Platform automations, and Power BI reporting. Responsibilities include Azure Data Factory pipeline development, Logic Apps and Power Automate workflows, Power BI modeling and publishing, Azure resource administration, governance, monitoring, troubleshooting, security controls, documentation, and performance optimization.

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.

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.