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 and implement modern data warehouse solutions using Databricks and cloud (Azure/AWS). Develop, orchestrate (Airflow) and troubleshoot ETL pipelines (batch and streaming), build dimensional data models for reporting, collaborate with architects and business stakeholders, mentor team members, and drive technical discussions.

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.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Build and maintain accounting data reporting and automation for global accounting teams. Design ETL/BI pipelines, investigate high-volume datasets, perform testing/QA, troubleshoot system configuration with users, and deliver accurate reports under tight deadlines.

Validate data platforms, ETL/ELT pipelines, transformations, and analytical models. Design and run data test plans, execute SQL checks, use Databricks/Delta Lake/Spark for verification, and build automated data tests with Python/PySpark/Pandas. Collaborate with engineers, product owners, and SMEs to find root causes and ensure data quality across delivery.

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.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

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.

The role involves data modeling, troubleshooting complex issues, optimizing storage architectures and mentoring junior engineers while working with GCP and Python.

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.

Design, build, and operate scalable ETL/ELT pipelines using open-source tools. Optimize databases and SQL, prototype new tools, support migration from Azure Databricks to AWS, ensure data quality, observability, CI/CD, and collaborate across distributed teams in Advertising and Market Research.

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.

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.

The Data Engineer will design and maintain data pipelines, build data warehouses, and ensure data quality while collaborating with data scientists and engineers.