Top Data Engineer Jobs in Hyderabad

Supports the design, development, testing, and maintenance of Microsoft-based data solutions. Builds and monitors Azure data pipelines, performs data ingestion and transformation, writes SQL queries and scripts, and manages data across SQL Server, Azure SQL, data lakes, and cloud platforms. Collaborates with analytics and business teams, implements data quality and security controls, documents technical processes, and supports Power BI reporting while developing modern cloud data engineering skills.

Designs and maintains AWS data architectures, ETL/ELT pipelines, data warehouses, analytics datasets, dashboards, and visualization solutions. Uses SQL, Python, Spark, Glue, Lambda, S3, Athena, Redshift, and QuickSight to prepare, transform, validate, and govern data. Collaborates with product, engineering, and operations teams, optimizing performance, cost, scalability, and data quality while integrating data systems with AI and natural-language query tools.

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 data foundations for AI agents, analytics, scorecards, and dashboards. Responsibilities include developing ingestion pipelines and enterprise connectors, modeling raw-to-mart layers, managing metadata and vector indexes, implementing data quality and lineage controls, and supporting governed data stores. The role also covers API integrations, incremental ingestion, security controls, testing, release activities, and data validation. Preferred experience includes RAG preparation, unstructured content processing, knowledge graphs, LLM applications, and regulated life sciences data.

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.

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.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated models, reconciliation, and lineage for an AI-native finance platform. Responsibilities include dbt development, Airflow or Dagster orchestration, SQL and warehouse performance tuning, Python tooling, data quality testing, integration of messy enterprise systems, and mentoring engineers. The role supports multi-cloud, multi-tenant, single-tenant, and on-premises deployments with strong requirements for correctness, auditability, and tenant isolation.

Develops, maintains, monitors, troubleshoots, and optimizes Amazon Redshift data warehouses and ETL/ELT pipelines. Responsibilities include SQL and Python development, production support, performance tuning, data quality validation, Tableau support, incident resolution, and operational documentation. The role also supports migration from Redshift to Databricks using Spark, Delta Lake, PySpark, Workflows, and Unity Catalog.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

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.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

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.

Owns data architecture and scalable engineering systems for an AI-native finance platform. Responsibilities include canonical data modeling, configurable connectors, SQL/dbt transformation, warehouse optimization, orchestration, CDC, lineage, data quality, schema evolution, and audit-ready traceability. The role also supports curated data for ML and LLM systems, customer-hosted multi-cloud deployments, mentoring distributed engineers, and collaboration with AI, backend, product, and US-based leadership.

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.

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 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.

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.

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.