Top Data Engineer Jobs in Kolkata

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, optimizes workflows, and improves performance. Acts as a subject matter expert, leads team decisions, mentors junior engineers, and coordinates cross-functional data initiatives. Requires at least five years of data engineering experience, proficiency with scalable pipelines, databases, cloud platforms, distributed computing, and data validation techniques.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, manages data migration across systems, optimizes workflows, and supports distributed data platforms. The role also involves making team decisions, collaborating across multiple teams, mentoring junior engineers, and aligning data strategies with organizational goals.

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate data validation, troubleshooting, and operational processes. Develop reusable frameworks, implement data quality and monitoring controls, optimize workloads, and support testing, deployment, productionization, and ongoing enhancements. Collaborate with business, data engineering, and platform teams to identify automation opportunities.

Design, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Investigates data quality issues, reconciles discrepancies, performs data lineage analysis, supports cloud migration initiatives, and validates data across systems. The role also provides production support, resolves incidents, develops reusable engineering components, and collaborates with stakeholders on data requirements.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, BigQuery data platforms, and Airflow orchestration. Investigates data quality issues, validates data across systems, performs lineage and source-to-target analysis, supports cloud migration initiatives, and resolves production incidents. Collaborates with stakeholders and engineering teams to define data requirements and develop reusable data engineering components.

Design and support batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Troubleshoot data quality, reconciliation, lineage, discrepancies, and production incidents. Support cloud data migrations, validate data across systems, collaborate with stakeholders, and develop reusable engineering components using BigQuery, SQL, Python, Airflow, and GCP services.

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.

Designs and maintains scalable AWS, Snowflake, and dbt data pipelines and transformation frameworks. Builds ETL/ELT workflows, dimensional models, data quality frameworks, and optimized SQL workloads for structured and semi-structured data. Responsibilities include pipeline orchestration, monitoring, troubleshooting, CI/CD, legacy ETL modernization, documentation, production support, and collaboration with technical teams. The role also contributes to architecture discussions, Agile delivery, code reviews, and mentoring junior engineers.

Build and maintain governed finance data products on Microsoft Fabric and Databricks. Responsibilities include developing PySpark and Python ingestion pipelines, implementing incremental loading and CDC, optimizing Delta Lake tables, managing Azure Data Factory pipelines and Analysis Services cubes, designing Power BI semantic models, and ensuring version control, testing, monitoring, governance, and data contract compliance. The role also involves stakeholder collaboration, mentoring junior engineers, and applying enterprise data architecture standards.

Build and support large-scale, real-time kdb+ analytics platforms processing billions of daily messages. Develop kdb Insights or KDB-X modules, packages, pipelines, performant q queries, and functions for trading desks. Create SRE tools to detect performance bottlenecks across q, Python, and Java processes running continuously in AWS. Support ingestion stability, Kubernetes deployments, PyKX data processing, and cloud-native analytics infrastructure.

Build and operate production data platforms, tooling, and infrastructure supporting autonomous pipeline delivery. Own monitoring, alerting, DataOps, Kafka, Kubernetes, Terraform, AWS, Databricks/Spark, dbt, and Airflow environments. Improve platform reliability, troubleshoot production issues, and partner with product, analytics, and infrastructure teams. Champion AI adoption and automation while enabling high-throughput, low-latency data processing and strong developer experience.

Designs, develops, tests, and maintains scalable ETL pipelines using Azure services and streaming technologies. Collaborates with architects, analysts, scientists, and business stakeholders to ensure data accuracy and integrity. Builds data models and documentation, optimizes workflows, monitors production data flows, troubleshoots issues, implements data-quality controls, and integrates cloud and on-premise storage while supporting governance, security, and privacy standards.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain data pipelines using Databricks, Python, SQL, and Spark. Apply data modeling, cloud data services, version control, and CI/CD practices. Support streaming pipelines, workflow orchestration, data governance, security controls, and downstream BI requirements. Experience with AWS or Azure is required, while Databricks certification, streaming technologies, orchestration tools, and Power BI exposure are preferred.

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 maintains conceptual, logical, and physical data models based on business requirements. Leads discovery sessions and design workshops, develops solution blueprints, creates modeling deliverables, and supports client sign-off. Collaborates with data and integration architects to ensure cohesive system integration. Provides physical design and DDL guidance to engineers, contributes to delivery estimation, and applies dimensional, 3NF, NoSQL, cloud database, data warehouse, and modern data platform expertise.

Contributes to data platform blueprint and architecture design, ensuring cohesive integration across systems and data models. Collaborates with integration and data architects, leads platform improvements, resolves technical issues, supports team decisions, and coordinates cross-functional initiatives. The role also involves mentoring team members, facilitating knowledge sharing, and optimizing data platform reliability and processes using SAS Analytics.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.