Top Data Engineer Jobs in Kolkata

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Lead modernization of a large-scale AWS data platform: design, build, and optimize scalable ETL/ELT pipelines, migrate Airflow/EMR to AWS-native services (Glue, MWAA), implement data lake/warehouse solutions, enforce data governance/lineage/metadata, build data quality and monitoring frameworks, automate deployments with IaC, and support BI (QuickSight) and Power BI migration, UAT, and production support.

Design, build, and maintain scalable cloud-native data pipelines and dimensional data models using DBT, SQL, and AWS services. Implement infrastructure-as-code with AWS CDK, automate CI/CD, ensure data quality and governance, optimize performance, and collaborate with cross-functional teams and offshore partners to deliver analytics-ready data assets.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

Design and implement critical processes for a subscription billing system while collaborating with cross-functional teams. Requires expert SQL skills and familiarity with various databases and DevOps methodologies.

As a Data Engineer, you will enhance data pipelines, support ML workflows, and improve cloud infrastructure on Azure and AWS, collaborating with Scientists and Engineers to turn data into actionable insights.

The Azure Data Engineer will build data pipelines and dataflows, implement security modules, and work with Azure services while supporting business requirements and data analysis.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.

Design, build, and maintain scalable batch and streaming ETL/ELT pipelines, production data services (REST/gRPC), and cloud-based data infrastructure. Implement CDC and time-travel data architectures, ensure data quality with tests and CI/CD, optimize data models for OLTP/OLAP, and collaborate cross-functionally to deliver reliable production data systems.

Responsible for designing and developing a GCP data ecosystem, creating data pipelines, optimizing data architectures, troubleshooting issues, and mentoring junior engineers.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse solutions.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

The role involves designing scalable data pipelines and architectures using Microsoft Fabric, client engagement for requirements gathering, and optimizing data solutions for analytics and reporting.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

Design, build, and maintain scalable ETL pipelines and data models for analytics and ML. Ensure data quality, optimize performance, enforce governance and security, and collaborate with stakeholders to deliver reliable, production-ready data for reporting and data science.

Deliver data engineering and analysis work using Python, PySpark, SQL and data platforms (Airflow, Trino, Hive). Produce LLD, coordinate offshore teams, follow Agile/Scrum, and work with container platforms (Linux, OpenShift, Kubernetes) and visualization (Superset). Banking domain experience is a plus.

Design, build, and maintain cloud data pipelines and streaming solutions using Java, Kafka, AWS services, SQL/warehouse technologies, and Python. Implement CI/CD, automated testing, and resilient microservice-based systems. Collaborate in a small agile team and ensure secure identity/authentication controls.

Design, implement and maintain high-volume online and offline batch and transactional data applications using Java and Spark. Build scalable data stores, write scripts, call APIs, and author complex SQL. Maintain code and database infrastructure, ensure data integrity, perform data cleaning, visualization and reporting, and support model training in an agile environment.