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, develop, and maintain Snowflake-based data solutions and ETL pipelines. Ensure data quality, optimize pipeline performance, document workflows, and collaborate with teams while mentoring juniors.

Design, develop, and maintain data pipelines and ETL processes, ensure data quality and migration across systems, troubleshoot complex issues, lead and mentor team members as an SME, and collaborate with cross-functional teams to deliver scalable data solutions.

Design, build, and maintain scalable data pipelines and ETL processes using AWS Analytics. Ensure data quality, implement data architecture and models, collaborate with data scientists and analysts, and optimize data solutions for performance and reliability.

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.

Design and build scalable ELT pipelines and data models using Databricks, dbt, Airflow and BI tools. Lead data governance, observability, and self-serve analytics initiatives; partner with data scientists, product and business stakeholders; mentor engineers and drive enterprise-wide data platform and architecture decisions to enable reliable, governed, and AI-enabled data products.

Design, build, and maintain scalable AWS-based ETL/ELT data pipelines and data platform solutions. Develop SQL and Python code, create tests and documentation, monitor and optimize pipeline performance, and collaborate with BI, AI/ML, and infrastructure teams while following best practices and data governance.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

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.

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, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Maintain and monitor Python-based web scraping configurations, detect and resolve scraping issues, coordinate with stakeholders, produce reports, and build ETL pipelines to ingest and transform data into the data lake.

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

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.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

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

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.