Top Data Engineer Jobs in Kolkata

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.

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.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

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.

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.

Build and manage scalable ETL pipelines, data models, Airflow workflows, and dbt transformations for Zoom’s marketing analytics infrastructure. Develop complex SQL and Python solutions, implement data quality monitoring, and collaborate with analysts and marketing stakeholders. The role supports campaign performance, attribution modeling, customer journey analytics, and reliable reporting through production-grade AWS data solutions.

Designs and maintains HR data infrastructure for People Analytics. Builds ETL pipelines, data models, Airflow workflows, dbt transformations, SQL queries, and data quality monitoring systems. Partners with analysts and stakeholders to deliver reliable, scalable datasets and workforce insights. Requires strong SQL, Python, AWS, Airflow, and dbt expertise, plus experience translating business requirements into production-grade data solutions.

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 and evolve a reliable, scalable data platform supporting analytics, credit models, experimentation, and operational reporting. Responsibilities include creating batch and near-real-time ingestion frameworks, data modeling and semantic layers, governance and security controls, lineage, PII handling, observability, and platform roadmaps. The role also involves scaling backend services, partnering with Product and Data Science teams, and supporting large distributed datasets using modern orchestration, warehouse, and programming technologies.

Manage Microsoft Fabric governance, security, access controls, capacity, job monitoring, compliance, and workspace hygiene. Implement data policies across Fabric and Azure, automate administrative and security tasks with APIs, scripts, and notebooks, and create operational dashboards, alerts, and compliance reports. Requires enterprise-scale data governance experience and hands-on expertise with Fabric administration, OneLake, Purview, Power BI, and security controls.

Design, build, and optimize scalable batch and streaming data pipelines on GCP and AWS. Develop ETL/ELT frameworks using SQL, Python, and Apache Beam; optimize BigQuery performance and costs; implement data quality, validation, monitoring, governance, lineage, and metadata tracking; and automate deployments through CI/CD. Collaborate with analytics, product, engineering, and business teams to deliver reliable data solutions for reporting and advanced 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.

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.

Extract and maintain support data, build pipelines and dashboards, analyze ticket trends and customer journeys, identify automation opportunities, and translate insights into prioritized improvements. The role implements Zendesk workflows and APIs, supports AI and automation initiatives, runs experiments, measures business impact, and communicates findings to technical and non-technical stakeholders.

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.