Top Data Engineer Jobs in Kolkata

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, model, and optimize relational and analytical data structures for OLTP and data warehouse systems. Build ER diagrams, define modeling standards, manage metadata with tools like Erwin/ER Studio, and drive data cataloging/lineage using Alation/Collibra. Perform performance tuning, sandbox POCs, handle time-series/history, and collaborate with engineers, architects, and product teams while documenting and enforcing governance.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Build and maintain large-scale PySpark/Spark data reconciliation pipelines on Hadoop/Big Data platforms; integrate with Kafka, APIs and microservices; support capital markets regulatory reporting and reconciliations; lead globally distributed teams, manage stakeholders, and deliver solutions with strong application design and optional UI integration.

Design, build, and optimize end-to-end data pipelines for large structured and unstructured data. Implement near-real-time ETL, data validation, monitoring, and performance optimization. Collaborate with stakeholders, document designs and workflows, and provide technical guidance to the team.

Design, build, optimize, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera (CDH). Collaborate with data teams, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for Spark applications.

Build and operate automated data quality checks, monitoring, alerting, and dashboards; integrate quality into data pipelines and governance tooling; automate anomaly detection and support auditors, analysts, and stewards to surface issues in near-real-time.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

Design, operate, and secure scalable batch and streaming data pipelines and platforms. Own ingestion, transformation, serving, data quality, and access controls. Partner with product, risk, compliance, and InfoSec to ensure production reliability, correctness, and audit readiness for financial data.

Design, build, and optimize scalable data pipelines and models using Spark, Kafka, Airflow, dbt, and SQL across GCP and AWS. Ingest and normalize marketing and analytics data, ensure data quality and governance, support analytics and reporting, and collaborate with cross-functional global teams.

The Snowflake Senior Data Engineer will lead projects in data engineering, utilizing Snowflake and various AWS services to optimize data workflows and storage, while also guiding the team and collaborating with stakeholders.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Design, build, and optimize scalable data warehouse and analytics solutions on Azure and Snowflake. Develop and maintain ETL/ELT pipelines, monitor production pipeline reliability and performance, support analytics users (Power BI), and drive data architecture and process improvements.

Design, build, and maintain Power BI dashboards and semantic data models using Power BI and Microsoft Fabric. Optimize performance, troubleshoot dataset and refresh issues, ensure governance and data accuracy, and collaborate with stakeholders to modernize analytics platforms.

Design and scale end-to-end data architecture and real-time/batch pipelines to unify product telemetry, CRM, and billing into a single source of truth. Build data foundations for ML (feature pipelines, training datasets, low-latency scoring), define telemetry/event contracts, implement data quality, monitoring, governance, and enable self-service analytics.

The Senior Data Analytics Engineer at MariaDB designs and builds data pipelines, writes SQL queries, develops internal tools, and translates data into actionable insights. They will collaborate closely with stakeholders and leverage AI tools to enhance analytics workflows.

As a Data Engineer, you'll build and monitor large-scale data pipelines, develop the DBT setup for data transformation and solve customer problems using your SQL skills.

The Senior Data Engineer will design and optimize ETL/ELT pipelines, manage data integration, perform data validation, and support reporting solutions using various technologies.

Design, build, monitor and scale an enterprise data warehouse and pipelines. Lead data integration, ETL design, source-to-target mapping, reusable frameworks, automated testing/CI, and partner with stakeholders to deliver secure, governed analytics for product, marketing and HR use cases.

As a Lead Data Engineer, you will design and manage data pipelines, develop ETL processes, and ensure data quality. Mentorship of junior engineers is also part of the role.

Develop and scale data management systems for AutoML and forecasting platforms, ensuring data reliability and implementing SLAs across pipelines.

The Senior Data Scientist & Engineer will build and manage data infrastructure, analyze product usage, build models, track metrics, run experiments, and communicate insights to guide product decisions.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.