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 data solutions including data pipelines and ETL processes. Ensure data quality, optimize pipelines, collaborate with cross-functional teams, troubleshoot data issues, and support BI initiatives using MicroStrategy and related tools.

Design and implement scalable Snowflake architectures, build and maintain ETL pipelines, ensure data quality and performance, define data modeling standards, lead CoE initiatives, mentor teams, support presales and POCs, and integrate GenAI use cases (Cortex, LLMs, Streamlit) with the data platform.

Design, develop, and maintain scalable Snowflake data pipelines and ETL/ELT processes. Ensure data quality, performance tuning, and security. Collaborate with architects, data engineers, and business teams; contribute to CoE guidelines, POVs/POCs, and cross-project reference architectures. Support delivery teams and leverage Snowflake features (Snowpipe, Streams, Tasks, Stored Procedures) for analytics and reporting.

Design and implement scalable, secure Snowflake architectures and data pipelines. Lead data ingestion, ETL, transformations, performance tuning, security controls, and best practices. Mentor teams, support presales, run POCs, and standardize data modeling across projects using Snowflake features and integration tools like dbt and Fivetran.

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.

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, 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 and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Lead technical direction for data platforms, design and maintain data pipelines for reporting and AI (RAG, embeddings, vector stores), reduce architectural debt, mentor engineers, partner with product leadership, and represent Mitratech externally.

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.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Design, build, and maintain cloud-based ETL/data pipelines in Snowflake. Extract and transform data from heterogeneous sources, optimize SQL performance, implement monitoring, and collaborate across teams. AWS and Python experience are advantageous.

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.

Design, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

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.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Design, develop, and optimize PL/SQL artifacts and Python ETL scripts; tune SQL and Oracle performance; ensure data integrity and availability; integrate systems via APIs; support production deployments and monitoring; collaborate with engineers and analysts; administer Oracle and PostgreSQL; use GitLab and Jira for version control and tracking.

Develop and maintain data pipelines and applications using Python and Pandas, apply OOP and data structures, write and optimize SQL, and communicate with stakeholders. Familiarity with Agile, DevOps/CI-CD tools (Jenkins, Git, Jira), AWS, web frameworks (Django/Flask), and Snowflake is a plus.

Design, build and optimize ETL pipelines and cloud data workflows using Python, PySpark, AWS Glue and Lambda. Implement data models and schemas in PL/SQL/PostgreSQL, integrate AWS services (SNS, Step Functions), and troubleshoot pipelines for performance, scalability and reliability.

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.