Top Data Engineer Jobs in Kolkata

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, develops, and maintains data solutions, pipelines, and ETL processes using Snowflake. Ensures data quality, supports data migration across systems, optimizes cloud-based data workflows, troubleshoots complex data issues, and applies governance and security best practices. The role leads technical decisions, collaborates across teams, coordinates data initiatives, and mentors junior engineers.

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.

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.

Designs and refines data platform blueprints, collaborates with integration and data architects, troubleshoots platform issues, optimizes workflows, supports data quality, and mentors junior team members. Requires expertise in Databricks, cloud-based data processing, data integration, data modeling, scripting, automation, and large-scale data environments.

Designs and supports data platform blueprints and components, collaborating with Integration and Data Architects to ensure cohesive system integration and data models. Leads technical decisions, manages team performance, provides cross-team solutions, and facilitates knowledge sharing. Requires expertise in graph databases, data modeling, integration methodologies, cloud data platforms, and database performance optimization.

Develops and refines data platform blueprints and designs using Databricks. Collaborates with integration and data architects to align systems, data models, and platform components. Troubleshoots integration and performance issues, optimizes data processing workflows, documents best practices, and contributes to continuous improvement. The role requires experience with cloud-based data platforms, distributed computing, data integration, data modeling, scripting, automation, and large-scale data processing.

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.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Designs, builds, deploys, and supports secure, scalable cloud data solutions and pipelines for enterprise clients. Translates business requirements into data architectures, integration patterns, and models; ensures performance, reliability, governance, and documentation. Provides technical leadership during reviews and workshops, collaborates with engineering and analytics teams, contributes reusable accelerators and best practices, mentors colleagues, and manages client engagements through delivery.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Develop, optimize, and maintain scalable ETL/ELT data pipelines for structured and unstructured data using Python and PySpark. Ensure data quality through validation, cleansing, transformation, and reconciliation. Collaborate with technical and business stakeholders, troubleshoot performance issues, conduct code reviews, and implement coding standards and CI/CD pipelines. Work with big data frameworks, cloud platforms, databases, data models, schemas, and warehousing solutions.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.