Top Data Engineer Jobs in Kolkata

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 scalable data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, optimizes workflows, and improves performance. Acts as a subject matter expert, leads team decisions, mentors junior engineers, and coordinates cross-functional data initiatives. Requires at least five years of data engineering experience, proficiency with scalable pipelines, databases, cloud platforms, distributed computing, and data validation techniques.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, manages data migration across systems, optimizes workflows, and supports distributed data platforms. The role also involves making team decisions, collaborating across multiple teams, mentoring junior engineers, and aligning data strategies with organizational goals.

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate data validation, troubleshooting, and operational processes. Develop reusable frameworks, implement data quality and monitoring controls, optimize workloads, and support testing, deployment, productionization, and ongoing enhancements. Collaborate with business, data engineering, and platform teams to identify automation opportunities.

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.

Develop and analyze Guidewire insurance data across policy, claims, and billing domains. Build complex SQL queries, perform data extraction and transformation, validate source-to-target mappings, investigate data quality issues, and support reconciliation, reporting, testing, and downstream analytics. Collaborate with business, Guidewire, and technical data teams while working with Snowflake and large-scale datasets.

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.

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.

Designs and maintains scalable data ingestion, transformation, integration, and ETL/ELT pipelines. Diagnoses data quality issues, performs root-cause analysis, implements sustainable fixes, and optimizes SQL and data-processing workloads. Integrates internal and external systems through APIs and batch interfaces, supports CI/CD and DevOps practices, and ensures data accuracy, governance, security, and compliance. Collaborates with architects, analysts, QA teams, and stakeholders to deliver reliable data for analytics, reporting, and downstream applications.

Designs and maintains Salesforce Data Cloud solutions for audience segmentation, activation, real-time ingestion, and data governance. Responsibilities include developing SQL queries, configuring Data Extensions, Data Model Objects, Data Streams, Calculated Insights, and Computed Traits; architecting cloud integrations with Snowflake, AWS, Azure, or GCP; resolving data quality issues; and implementing consent and governance frameworks.

Designs and maintains scalable SQL and Python data pipelines, dimensional models, data marts, and enterprise Tableau dashboards. Manages Tableau Server or Cloud administration, extract refreshes, permissions, row-level security, performance tuning, and data quality governance. Partners with stakeholders to translate business needs into analytics solutions, supports CI/CD and documentation, and provides technical guidance. Requires advanced Tableau and SQL expertise, cloud data warehouse experience, Python-based ETL automation, and at least six years of relevant experience.

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.