Top Data Engineer Jobs in Hyderabad

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

Design and develop scalable Azure ETL/ELT pipelines and modern cloud data platforms. Build Databricks and PySpark solutions, implement Bronze/Silver/Gold Medallion Architecture, develop Airflow and dbt workflows, create analytics data models, and maintain CI/CD, governance, and data quality standards.

Develop and optimize SQL queries, data models, ETL pipelines, and data ingestion processes using Microsoft Fabric, Azure, Databricks, and Snowflake. Build Data Lakehouse and Medallion Architecture solutions, manage full and incremental loads, and support data warehouse systems. Create dashboards with Power BI or Tableau, document workflows, and collaborate with data analysts and software engineers.

Design, develop, and maintain scalable data pipelines, ETL/ELT processes, integrations, and cloud-based data solutions. Optimize data storage, retrieval, workflows, and processing performance while ensuring data quality and reliability. Collaborate with cross-functional teams, troubleshoot complex engineering challenges, lead technical decisions, and mentor junior team members.

Build and deploy enterprise AI applications, data pipelines, and GenAI solutions using Python, LLMs, and RAG. Develop backend services and microservices integrating REST APIs, SQL, NoSQL databases, and enterprise data platforms. Optimize AI models for production environments while solving complex technical problems and collaborating effectively with stakeholders.

Designs, builds, and maintains enterprise data pipelines from Dynamics 365 F&O into ADLS Gen2, Azure Synapse, and Microsoft Fabric. Develops Spark/PySpark transformations, scalable data models, and curated analytics datasets while ensuring data quality, governance, lineage, and reliability. Analyzes AxDB and finance data structures, supports Dataverse integrations, and collaborates with Finance, Operations, and BI teams to deliver reporting and subscription-based analytics solutions.

Develop scalable Java and Spring Boot backend services, REST APIs, microservices, and Spark-based data pipelines. Build Snowflake data solutions, optimize ETL/ELT workflows, and create React or Angular enterprise dashboards. Integrate frontend and backend systems, implement testing, monitoring, CI/CD, and DevOps practices, and troubleshoot production issues. Collaborate with engineering, data, DevOps, and business teams on architecture, code reviews, performance tuning, and root-cause analysis.

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.

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.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

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.

Leads client-facing data, analytics, AI, and platform modernization engagements using Microsoft technologies. Designs secure, scalable, governed data platforms; develops pipelines, data models, semantic models, and Power BI solutions; facilitates workshops; manages technical risks; mentors consultants; and translates business needs into practical data solutions.

Designs and maintains scalable data pipelines using AWS, Spark, Kafka, Airflow, and related technologies. Collaborates with product, data science, and business teams; develops reporting dashboards; documents data flows and runbooks; optimizes workflows; troubleshoots data processing issues; and supports Agile delivery and CI/CD practices.

Leads the development of scalable ETL and ELT pipelines using Azure Databricks, PySpark, SQL, and Delta Lake. Builds real-time and batch data solutions, implements Unity Catalog governance, develops automated unit tests, and resolves production issues end to end. Collaborates across scrum teams on database programming and platform modernization while applying SOLID principles and maintaining data integrity.

Lead the design, development, and optimization of scalable GCP data pipelines, ETL/ELT workflows, BigQuery warehouses, and streaming solutions. Migrate on-premise systems to GCP, ensure data quality, security, governance, and reliability, and troubleshoot large-scale data platforms. Collaborate with technical and business stakeholders, establish data engineering best practices, and mentor junior engineers while guiding complex projects and recommending platform improvements.

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.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

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.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

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.

Design, build, and maintain scalable ETL/ELT data pipelines and Snowflake-based data warehouses on AWS. Implement ingestion, transformation, orchestration, data quality, performance tuning, and monitoring; collaborate with stakeholders and troubleshoot production data platform issues.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

Design, develop, and maintain scalable batch and real-time data pipelines using Python and PySpark. Build cloud-based data processing solutions, CI/CD pipelines, and streaming systems while ensuring data quality, performance, and scalability. Collaborate with cross-functional teams to translate business requirements into reliable data architectures. The role involves technologies including Kafka, Flink, Kubernetes, Hadoop, Spark, MongoDB, and cloud platforms such as Azure, AWS, or GCP.