Top Data Engineer Jobs in Kolkata

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes using Databricks. Ensures data quality, supports data migration and deployment, optimizes workflows, troubleshoots pipeline issues, documents processes, collaborates with stakeholders, and mentors junior team members.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, migrate and deploy data across systems, optimize pipeline performance, and collaborate with stakeholders on data requirements. The role also involves data modeling, database design, cloud data services, and contributing independently as a subject matter expert.

Design, develop, and maintain data solutions, pipelines, data models, and ETL processes. Ensure data quality, reliability, and efficient data migration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, and serve as a subject matter expert. Monitor and troubleshoot pipeline performance while supporting analytical and operational data needs.

Build, maintain, and optimize scalable data pipelines, ETL/ELT processes, data lakes, and real-time streaming systems. Use Databricks, Spark, PySpark, Python, cloud platforms, and orchestration tools to ingest, transform, and distribute structured and unstructured data. Support data governance, security, performance optimization, troubleshooting, and collaboration with architects, analysts, data scientists, and engineers.

Build, maintain, and optimize scalable data pipelines, ETL/ELT processes, data lakes, and real-time and batch processing systems. Design cloud-based data platform solutions using Databricks, Spark, PySpark, and cloud storage services. Ensure data quality, security, governance, and performance while collaborating with architects, data scientists, analysts, and engineers. Support data ingestion, orchestration, troubleshooting, CI/CD, and transformation workflows across large-scale structured and unstructured datasets.

Build and optimize scalable data pipelines, ETL/ELT processes, data lakes, and real-time and batch processing solutions. Use Databricks, Spark, PySpark, cloud platforms, Python, SQL, orchestration tools, and streaming technologies. Ensure data quality, security, governance, availability, and performance while collaborating with architects, data scientists, analysts, and engineers.

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

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.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, Python, SQL, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support Databricks platform design, cloud-native storage, orchestration, governance, security, and performance tuning. Collaborate with architects, data scientists, analysts, and engineers to ensure reliable, secure, and accessible data across systems.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, Python, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Collaborate with architects, data scientists, analysts, and engineers to ensure data quality and availability. Support performance tuning, governance, security, orchestration, CI/CD, and cloud-native data storage solutions.

Designs and maintains data models across conceptual, logical, and physical stages. Partners with architects and clients to translate business requirements into data platform blueprints, mappings, and implementation-ready DDL. Leads discovery activities, design workshops, solution scoping, estimation, and stakeholder sign-off. Requires expertise in dimensional, 3NF, and NoSQL modeling, cloud database design, data warehouses, modern data platforms, and modeling tools such as Erwin or ER/Studio.

Designs and maintains data platform components, pipelines, integrations, and data models using Databricks. Collaborates with architects and cross-functional teams to implement scalable data solutions, security and privacy controls, governance practices, and performance optimization. Monitors and troubleshoots platform performance while contributing independently as a subject matter expert.

Designs and refines data platform blueprints using Snowflake, collaborating with integration and data architects to ensure cohesive systems and data models. Leads performance, scalability, troubleshooting, security, and reliability efforts while coordinating cross-functional initiatives. Acts as a subject matter expert, makes team decisions, provides solutions across teams, and mentors engineers.

Contributes to data platform blueprint and architecture design, ensuring cohesive integration across systems and data models. Collaborates with integration and data architects, leads platform improvements, resolves technical issues, supports team decisions, and coordinates cross-functional initiatives. The role also involves mentoring team members, facilitating knowledge sharing, and optimizing data platform reliability and processes using SAS Analytics.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.