Top Data Engineer Jobs in Hyderabad

Supports data onboarding, pipeline monitoring, troubleshooting, maintenance, validation, metadata management, lineage documentation, and cloud platform operations. Develops Python-based ingestion and automation scripts, assists with AWS deployments and monitoring, participates in incident response, and documents operational procedures. Collaborates with senior engineers while working with Amazon S3, AWS Glue Data Catalog, CloudFormation, Databricks, and related data technologies.

Develop and maintain GCP-based ELT pipelines using dbt and BigQuery, migrate SAS transformation logic and datasets to GCP, build SQL data transformations and dimensional models, and perform data validation, reconciliation, and quality testing. The role also involves managing data engineering projects, guiding teams, supporting migration activities, and driving continuous process improvement.

Own end-to-end batch and real-time data pipeline delivery using Databricks, Spark, Python, PySpark, dbt, and Kafka. Design transformation architecture, CI/CD pipelines, DataOps and observability frameworks, and administer Databricks environments. Lead streaming solutions, deployments, migrations, and upgrades while optimizing performance, access, and cost. Partner with stakeholders on requirements and technical trade-offs, mentor engineers, and lead code and design reviews.

Lead the design, development, deployment, and support of scalable data pipelines and platforms across on-premises and cloud environments. Build batch and near-real-time processing frameworks, modernize ETL and data warehouse workloads, implement governance and observability capabilities, troubleshoot production issues, and drive lakehouse adoption. Mentor engineers, collaborate with architects and stakeholders, evaluate emerging technologies, and ensure adherence to security, quality, and operational standards.

Lead the architecture and engineering standards for scalable data platforms and pipelines. Provide technical direction, conduct design reviews, and oversee Databricks administration, CI/CD, DataOps, observability, deployment, migration, and upgrades. Resolve complex platform challenges, mentor engineers, strengthen code quality, and partner with stakeholders on roadmaps and technical trade-offs. The role requires deep expertise in Databricks, Spark, Python, PySpark, dbt, and engineering governance.

Lead Salesforce data migration activities, including strategy, mapping, transformation, extraction, cleansing, deduplication, validation, loading, testing, reconciliation, and data quality checks. Collaborate with architects, developers, and business stakeholders while supporting integrations with ERP, marketing, and data platforms.

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.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Designs, develops, and implements large-scale data engineering solutions using agile, DevOps, cloud, and product-model practices. Works with Azure data lakes, analytics platforms, data pipelines, cloud services, databases, scripting, and CI/CD tools. Responsibilities include development, administration, and support across Azure Data Factory, Databricks, Blob Storage, Functions, Event Hubs, Data Explorer, Terraform, GitHub, and Azure DevOps.

Build and operate scalable data pipelines, warehouses, models, and quality controls for analytics and data products. Manage ELT/ETL infrastructure, governance, monitoring, anomaly detection, and root-cause remediation. Own the production lifecycle of a computer-vision cart classifier, including data workflows, feature engineering, evaluation, deployment, drift monitoring, and integration. Collaborate with BI, engineering, product, and machine learning teams.

Design and build scalable data solutions, pipelines, enterprise data models, and analytics or AI/ML integrations using Palantir Foundry. Develop Foundry applications and custom TypeScript frontends, optimize distributed systems, and use AWS, Snowflake, Python, PySpark, and modern DevOps tools. The role requires strong data engineering experience, hands-on Palantir Foundry expertise, and the ability to deliver complex solutions in agile environments. Insurance industry experience and Palantir certification are preferred.

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.

Build and maintain GCP-based data pipelines, data products, and AI/ML applications using services including BigQuery, Vertex AI, Dataflow, and Dataproc. Develop transformations with Python, SQL, and PySpark; implement CI/CD, monitoring, testing, and observability; ensure data quality and reliability; troubleshoot pipeline issues; and support stakeholders with data requirements, analytics, and reporting.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Design, develop, and support scalable cloud-based data pipelines, data products, and AI-enabled applications. Build batch and real-time ETL/ELT solutions, implement data quality and observability controls, and support CI/CD and production operations. Collaborate with technical and business stakeholders to deliver secure, reliable data solutions. Troubleshoot incidents, perform root-cause analysis, enhance existing platforms, and contribute to modernization, documentation, and engineering standards.

The Data Platform Engineer assists with data platform blueprint and design, integrating systems and data models with Integration and Data Architects. Responsibilities include leading teams, making technical decisions, solving cross-team problems, optimizing data workflows, and sharing knowledge. The role requires strong Snowflake expertise, SnowPro Core Certification, data modeling, ETL, cloud data architecture, SQL, and at least 7.5 years of experience. The position is based in Gurugram.

Assists with data platform blueprint and architecture design, collaborating with integration and data architects. Develops ontologies, taxonomies, semantic models, metadata schemas, and knowledge graphs using RDF, OWL, SKOS, and SHACL. Designs scalable linked-data architectures, supports semantic search and AI-driven recommendations, and manages graph databases and query systems. The role also involves integrating domain standards, improving data discovery, and communicating technical concepts to stakeholders.

Assists with data platform blueprint and design, including platform components, data modeling, database optimization, and integration. Collaborates with Integration and Data Architects to ensure cohesive systems and data models. Acts as a subject matter expert, manages team decisions, provides cross-team solutions, facilitates knowledge sharing, and monitors team performance.

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.

Designs and supports data platform blueprints, data models, and platform components. Collaborates with integration and data architects to ensure cohesive system and data integration. Acts as a subject-matter expert, leads teams, makes technical decisions, provides solutions across teams, and promotes knowledge sharing. Requires expertise in data modeling, data architecture, data integration, cloud data platforms, and scalable data model implementation.

Designs, develops, tests, implements, and supports scalable data solutions on Microsoft Azure and Fabric. Builds optimized pipelines, data models, incremental and full-load processes, distributed processing workflows, reusable frameworks, and analytics platforms. Responsibilities include SQL performance tuning, PySpark and Delta Lake development, DevOps CI/CD, Power BI dashboard publishing and support, solution security and resilience, and mentoring peers.

Manage the Microsoft Fabric analytics environment, including workspaces, security, pipelines, Lakehouse layers, data models, and data quality monitoring. Develop and optimize Power BI dashboards, reports, and datasets using SQL, DAX, Power Query, and semantic modeling. Collaborate with technical and business stakeholders to translate requirements into actionable insights while ensuring performance, reliability, documentation, and maintainability.