Top Data Engineer Jobs in Hyderabad

Design, build, and maintain scalable ETL/ELT data pipelines on GCP/BigQuery using Python and Spark. Integrate systems (APIs, Oracle EBS), implement workflow orchestration, ensure data quality and security, monitor performance, and document processes while collaborating with analysts and business teams.

Build and maintain scalable ETL pipelines on GCP, transform large/disconnected datasets, automate and optimize data delivery, support stakeholders, and apply data engineering best practices including testing and release management.

Design, build, and maintain scalable ETL/ELT pipelines and microservices for marketing and product data, ingesting from ad platforms (especially Meta Ads) and event systems. Enable campaign performance tracking, lead funnel analysis, attribution, and revenue reporting. Ensure data quality, observability, governance, and documentation while collaborating with marketing, growth, and analytics to deliver actionable datasets and drive business outcomes.

Design, build, and optimize Snowflake data models and dbt pipelines. Create Power BI reports and paginated RDLs with DAX and row-level security. Manage CI/CD with Azure DevOps, version control with Git, enforce data quality and governance, and optimize warehouse cost and performance while collaborating with business stakeholders.

Data Engineer responsible for building and maintaining ETL pipelines, data processing workflows, and data platforms. Must have strong hands-on experience with Healthcare Revenue Cycle Management and understanding of healthcare data workflows. Role requires hybrid (onsite + offshore) work and immediate availability.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and optimize orchestrated data pipelines using dbt, Airflow, Azure Databricks, PySpark, SQL and Python. Implement CI/CD and automation, ensure data quality, perform ad-hoc analysis and reporting, collaborate with cross-functional teams, and troubleshoot DAG failures to support data-driven decision-making.

As a Data Engineer, you will design and maintain systems for processing large datasets, develop data pipelines, ensure data integrity, and optimize workflows for analytics and machine learning.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Build and optimize backend systems for real-time and historical data querying. Responsibilities include developing query language runtimes, parsing, semantic analysis, query planning, execution, Kafka-native streaming pipelines, Spring Boot services, and high-performance Java and C++ components. Collaborate on database infrastructure, query optimization, language semantics, type systems, code reviews, and engineering standards.

Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

Develops full-stack AI and machine learning applications using web technologies, statistical modeling, deep learning, databases, cloud platforms, and APIs. Designs algorithms, translates technical specifications into code, performs testing and debugging, documents solutions, and improves product and system performance. Works independently on complex initiatives, collaborates with stakeholders, mentors colleagues, and may manage projects or review others’ work.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

The Software Engineer will manage data collection, extend cloud infrastructure, and collaborate on datasets for AI models in Speechify's data team.

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.

The Data Engineer role involves designing and maintaining ETL pipelines, optimizing data storage solutions, collaborating with teams, and ensuring data accuracy and integrity.

Design, develop, and modernize enterprise-scale Azure data platforms. Lead solution architecture, refactor and optimize PySpark/Databricks pipelines, implement ETL/ELT and lakehouse/warehousing solutions, ensure performance, data quality, governance, and mentor junior engineers.

Design, build, and maintain scalable Java Spring Boot backend microservices integrated with blockchain/Web3 infrastructure. Develop REST APIs, event-driven systems (Kafka/JMS), SQL/NoSQL data stores, and containerized cloud deployments (Kubernetes). Ensure performance, security, and reliability while collaborating with cross-functional teams in Agile environments.

Design, build, and optimize large-scale ETL pipelines and distributed data applications (batch and streaming) using Spark and Scala. Ensure data quality and performance, deploy and manage workloads in cloud/container environments, troubleshoot production issues, and collaborate with product, engineering, and analytics teams to deliver scalable AdTech data solutions.

Design, build, and optimize scalable data pipelines and data platforms using PySpark, Databricks, Azure Data Factory, SQL, and Delta Lake. Develop ingestion, orchestration, transformations, data models, and large-scale processing workflows. Ensure performance, reliability, data quality, consistency, and governance while collaborating with analysts, data scientists, and business stakeholders.

The Data QA Engineer will apply the testing life cycle, create QA documentation including test plans, cases, summary reports, and traceability matrices, and validate data using SQL. Responsibilities include testing data warehouse and modeled data, using reporting tools, executing test cases with appropriate test data, supporting UAT, analyzing results, and communicating effectively. The role is remote and requires four hours of overlap with EST working hours.

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Lead and mentor a data engineering team to design, build, and optimize scalable batch and real-time data pipelines using Databricks, Spark, Delta Lake and cloud services (Azure/AWS). Implement Medallion architecture, ETL/ELT workflows (ADF/Databricks Workflows), CI/CD and IaC, ensure data governance, performance, and stakeholder collaboration.