Top Data Engineer Jobs in Kolkata

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, reliability, and performance across systems while supporting data migration and deployment. Collaborates with cross-functional teams, documents workflows, troubleshoots data issues, and optimizes distributed data-processing jobs. The role also provides technical guidance to junior team members and contributes as a subject matter expert.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, optimize data processing performance, troubleshoot data issues, document workflows, and collaborate with cross-functional teams. The role also involves independently contributing as a subject matter expert and supporting junior team members.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build and support scalable healthcare data platforms, unified data models, datasets, and ad hoc data access solutions. Extract and integrate heterogeneous healthcare data, improve data quality, support acquisitions, and model data for BI, software applications, machine learning, and AI. Collaborate with product, engineering, business, and clinical stakeholders while following software development lifecycle best practices.

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.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Build and maintain scalable ETL/ELT pipelines, cloud data infrastructure, automated workflows, and cross-platform integrations. Manage relational databases and cloud warehouses, optimize data access, and route clean data to AI systems, dashboards, and reporting tools. Implement data quality checks, deduplication, monitoring, governance, and security controls. Collaborate with AI, backend, and marketing teams while managing multiple client integration projects remotely.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Build and maintain scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

Senior Data Engineer responsible for making raw data usable and accessible across the organization. The role involves managing complex data engineering projects, contributing to policies and procedures, recommending improved processes and models, developing data acquisition, integration, and provisioning capabilities, and mentoring less experienced colleagues.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

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.

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.