Top Data Engineer Jobs in Kolkata

The Data Engineer will design, develop, and maintain ETL/ELT data pipelines, leveraging PySpark and Databricks to process large datasets, ensuring data quality, reliability, and performance optimization.

Design, develop, and maintain cloud-based data solutions and ETL pipelines using Microsoft Azure Data Services. Ensure data quality, enable data migration and deployment across systems, optimize pipeline performance and scalability, collaborate with cross-functional teams, lead and mentor team members, and serve as an SME driving technical decisions.

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, 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.

Design, build and maintain data solutions using Oracle SQL/PLSQL and Informatica ETL. Develop backend and APIs with Java/Spring Boot, work with XML/JSON, web frameworks (Angular/React), and operate in Unix environments. Use cloud/container tooling (AWS, Kubernetes, Docker, Rancher), Splunk for logging, follow Agile, collaborate across distributed teams, and support migrations and CI/CD pipelines.

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.

Design, build, and maintain ETL pipelines and frameworks on AWS. Deploy and troubleshoot containerized applications (Docker, EKS), support CI/CD and build tooling, work with SQL/Oracle and ORM tools, and use job schedulers like Autosys/Airflow. Collaborate in Agile/SCRUM environments.

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, 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 ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

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, 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.

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 ELT/ETL pipelines and data models using SQL, Python, and cloud services (AWS). Enable cloud data movement to Snowflake, perform SQL performance tuning, support code migrations, and collaborate in agile, distributed teams to deliver analytics-ready data solutions.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data, monitor and validate data flows, collaborate with cross-functional teams, and document data workflows and governance.

Design, build, and maintain scalable data pipelines and ETL processes using Python and PySpark. Orchestrate workflows with Airflow, query data with Trino and Hive, and write efficient SQL. Collaborate in Agile/Scrum teams to deliver data solutions and optimize data platform performance.

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.

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.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

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 scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

Data Engineer responsibilities include designing and building scalable data management systems, optimizing data pipelines, managing data warehouses, and ensuring data quality and compliance.