Top Data Engineer Jobs in Kolkata

Build and maintain scalable batch and real-time data pipelines, ETL/ELT processes, data lakes, and big-data processing frameworks. Use PySpark, Spark, Python, SQL, cloud platforms, and orchestration tools to ingest, transform, secure, and optimize data. Collaborate with architects, data scientists, analysts, and engineers to ensure data quality, availability, governance, and performance across high-volume workloads.

Develop and maintain data pipelines, ETL processes, and data platform solutions using Databricks. Collaborate with integration and data architects to align platform components with business requirements, troubleshoot technical issues, optimize processing performance, and support data quality and workflow efficiency. The role also involves contributing to platform design, participating in technical discussions, and mentoring junior team members.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, Python, SQL, and ETL/ELT tools. Design cloud-based data platforms, data lakes, ingestion strategies, and Big Data processing frameworks across AWS, Azure, or GCP. Ensure data quality, security, governance, performance, and availability while collaborating with architects, analysts, data scientists, and engineers.

Design, develop, and maintain data solutions, pipelines, data models, and ETL processes. Ensure data quality, reliability, and efficient data migration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, and serve as a subject matter expert. Monitor and troubleshoot pipeline performance while supporting analytical and operational data needs.

Build scalable Snowflake data pipelines and ETL/ELT processes across projects. Ensure data quality, security, performance, and support structured and semi-structured data for analytics. Collaborate with architects, engineers, business teams, and Team Leads to translate requirements into solutions. Contribute to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and GenAI initiatives using Snowflake Cortex capabilities.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, monitor and optimize pipeline performance, gather data requirements, and collaborate with stakeholders. The role requires independent execution, team participation, and contribution as a data engineering subject matter expert.

Build scalable Snowflake data pipelines and ETL/ELT processes for structured and semi-structured data. Ensure data quality, security, and performance through advanced SQL, Snowpipe, Streams, Tasks, and stored procedures. Collaborate with architects, engineers, and business teams to translate requirements into solutions, support analytics and reporting, contribute to CoE guidelines and frameworks, and participate in proof-of-concept initiatives.

Design, develop, and maintain data solutions and ETL pipelines using Snowflake and related tools. Ensure data quality, lead cross-functional data strategy, mentor junior engineers, resolve complex data issues, and manage team decisions for large-scale data migrations and deployments.

Build scalable, secure Snowflake data pipelines and ETL/ELT processes across projects. Develop solutions using structured and semi-structured data, optimize Snowflake performance, write advanced SQL, ensure data quality and security, and collaborate with architects, engineers, and business teams. The role also contributes to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and delivery-team support while tracking emerging Snowflake and GenAI capabilities.

Build and optimize scalable data pipelines, ETL/ELT processes, data lakes, and real-time and batch processing systems. Use Databricks, Spark, PySpark, Python, cloud platforms, orchestration tools, and streaming technologies to ingest and transform data. Collaborate with architects, analysts, data scientists, and engineers to ensure data quality, security, governance, availability, and performance across large-scale data environments.

Design and lead scalable Snowflake data architectures across ingestion, storage, transformation, and access. Establish data modeling standards, security practices, performance tuning, and architecture guidelines. Mentor teams, lead proofs of concept, contribute to centers of excellence, and support presales through proposals, estimations, solutioning, and client presentations. Collaborate with integration and data architects while evaluating Snowflake and generative AI capabilities.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes using Databricks. Ensures data quality, integrates and migrates data across systems, troubleshoots pipeline issues, and optimizes performance and reliability. The role collaborates with cross-functional teams to translate data requirements into technical solutions, documents workflows, supports data governance and security practices, and mentors junior team members.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support data platform architecture, governance, security, performance tuning, and deployment across AWS, Azure, or GCP. Collaborate with data scientists, analysts, architects, and engineers to ensure reliable, secure, and accessible data.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Microsoft Azure Databricks. Ensure data quality, integration, migration, deployment, and workflow performance across systems. Lead data engineering efforts, resolve complex data challenges, coordinate with stakeholders, make team decisions, and mentor engineers while contributing to cross-team initiatives.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

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.

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

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.

Designs and maintains SAP BW/4HANA data models and pipelines, develops end-to-end BI solutions, integrates SAP and non-SAP data, and delivers structured datasets for reporting and analytics. The role includes ETL, data transformation, performance optimization, query and load monitoring, and collaboration with business stakeholders. Candidates need strong SQL, data warehousing, SAP Datasphere, SAP BW on HANA, and SAP BDC experience. Power BI, Business Objects, and ABAP are beneficial.