Top Data Engineer Jobs in Hyderabad

Design and optimize Snowflake data solutions for a business rules modernization initiative. Responsibilities include refactoring SQL stored procedures, optimizing queries, designing Rules Catalog and Audit Repository databases, developing data pipelines, supporting integrations with Drools and Camunda, ensuring data integrity and scalability, and collaborating with architects, analysts, and application teams. The role also provides technical guidance and mentorship to junior engineers.

Build and maintain Python- and SQL-based data pipelines, perform ingestion, transformation, validation, and support analytics datasets and reporting. Use AI-assisted tools for development, debugging, optimization, and documentation. Collaborate on Fabric and lakehouse platforms while contributing to reusable data frameworks, automation, and business KPI reporting.

Design, build, and optimize scalable ETL/ELT pipelines and distributed data platforms using Databricks, Apache Spark, Python, Java, and SQL. Develop ingestion, transformation, validation, monitoring, and data quality frameworks for large datasets. Tune Spark jobs and SQL queries, troubleshoot production issues, support cloud-based architectures, and collaborate with cross-functional teams. The role also involves mentoring junior engineers and contributing to engineering standards, governance, security, and operational excellence.

Designs, develops, and maintains scalable data pipelines, data architectures, quality processes, and integrations across varied sources. Troubleshoots data issues, monitors solutions to meet SLAs, documents infrastructure, and collaborates with stakeholders and cross-functional teams. Requires strong SQL, database management, dimensional and relational modeling, and Python or Java experience, with preferred expertise in cloud data warehouses, big data, ETL tools, orchestration, BI dashboards, and Agile delivery.

Designs, develops, and maintains scalable Azure data platforms, including ETL/ELT pipelines, data lakes, warehouses, and medallion architectures. Integrates structured and unstructured data from diverse sources, optimizes SQL and Spark processing, and ensures data quality, governance, security, and compliance. Monitors production workflows, troubleshoots performance issues, and implements CI/CD and infrastructure automation while collaborating with analysts, DevOps teams, and business stakeholders.

Lead design and implementation of scalable ETL/ELT pipelines, ensure data quality and governance, build dashboards and automated reporting, mentor engineers, troubleshoot production incidents, and improve platform reliability, performance, and observability to support analytics and business KPIs.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

Develops and manages scalable data pipelines using Snowflake, SQL, dbt, and Airflow; builds Python-based REST APIs and microservices; supports Salesforce Data Cloud; implements CI/CD pipelines; designs reusable Slack app frameworks; and collaborates cross-functionally on high-quality data solutions.

Transforms raw data into high-quality datasets through ingestion, transformation, modeling, and validation. Supports low- to moderately complex business problems while receiving guidance from senior data leaders. Combines data engineering, analytics, and business acumen to build accessible data workflows that support organizational decision-making.

Leads the design and implementation of scalable data engineering frameworks, cloud data architectures, real-time pipelines, and data platforms. Establishes standards for data quality, governance, security, metadata, lineage, and compliance. Mentors data engineers, evaluates emerging technologies, collaborates with stakeholders and data modelers, and implements Azure-based architectures with CI/CD and DataOps practices. The role requires extensive experience in data engineering, insurance data operations, modeling, warehousing, streaming, and enterprise transformation programs.

Lead and manage data engineering and BI initiatives, develop and oversee data pipelines and reporting (Power BI), collaborate with stakeholders across functions, and mentor engineering staff. Role sits within KPMG India professional services environment.

Design, build, and maintain reports, dashboards, datasets, and data pipelines across on-prem and cloud (Snowflake/Lakehouse). Transform structured and unstructured data for analytics and AI/ML, ensure data governance and security, troubleshoot refresh/connectivity issues, optimize performance, translate business needs into technical solutions, and document processes.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Architect and build AI-native data pipelines, semantic layers, KPI services, vector retrieval infrastructure, and governed data access on Azure. The role requires production-scale Spark and Databricks engineering, RAG and embedding pipelines, data quality, CI/CD, testing, lineage, auditability, and collaboration across engineering, governance, and business teams.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Designs, develops, and governs enterprise data platforms using Databricks and Microsoft Fabric. Builds scalable pipelines, lakehouses, warehouses, semantic models, data marts, automation frameworks, and data-quality validation systems. Leads data engineering through the full delivery lifecycle, implements security and governance controls, supports CI/CD and infrastructure automation, optimizes Spark workloads, and collaborates with stakeholders and technical teams. The role also includes mentoring engineers and establishing reusable engineering standards.

The Data Engineer role involves data analysis, creating data models, and collaborating with business partners. Applicants need strong technical and analytical skills, particularly in SQL and data modeling.

Design, develop, and maintain scalable ETL pipelines and data models to support payments, reconciliation, chargeback tracking, fraud analysis, and real-time monitoring. Manage and optimize large-scale data systems in Oracle and SQL Server, integrate payment platforms (Worldpay, Adyen, Stripe), build dashboards and automate reporting, troubleshoot data discrepancies, document workflows, and collaborate with stakeholders to shape data strategy and improve engineering practices.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Develop and support AWS cloud-native data lakes and ecosystems, scalable Databricks data pipelines, and enterprise data solutions. Configure and optimize Databricks clusters and jobs, implement production-grade pipelines using PySpark and SQL, and work with services including Glue, Athena, Redshift, and PostgreSQL. Apply data security and privacy practices, provide production support, improve team processes, and collaborate in a global onshore-offshore delivery environment.

Leads the design, development, implementation, delivery, and support of enterprise Quantexa solutions. Provides technical direction across architecture, Entity Resolution, graph processing, ETL, scoring, alerting, performance tuning, CI/CD, and production support. Mentors developers and data engineers, establishes engineering standards, conducts technical reviews, manages risks and technical debt, and collaborates with stakeholders to deliver scalable, secure, high-performing data solutions.

Lead design and build scalable GCP data pipelines and ETL/ELT processes using BigQuery, DBT, Python, and Airflow. Ensure data quality, optimize performance, mentor junior engineers, collaborate with cross-functional teams, and implement best practices for analytics-ready datasets and CI/CD workflows.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.