Top Data Engineer Jobs in Hyderabad

Develop scalable batch and real-time data pipelines using Python and Azure data services. Integrate industrial systems such as SCADA, MES, Historians, and IIoT platforms with enterprise and cloud environments. Design data lake architectures, optimize performance, security, and scalability, apply data modeling expertise, troubleshoot complex issues, collaborate with stakeholders, and produce technical documentation.

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.

Design, develop, and deploy enterprise data pipelines and data products using AWS and Databricks. Build Medallion Architecture pipelines, write Python, PySpark, and SQL code, optimize data workflows, troubleshoot production issues, and implement CI/CD using Databricks Asset Bundles, GitHub Enterprise, and GitHub Actions. Work hands-on across the full data engineering lifecycle in an Agile/Scrum environment.

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.

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.

Develop and optimize data pipelines and workflows on GCP using BigQuery, dbt, and Airflow. Responsibilities include data modeling, advanced SQL, BigQuery tuning and cost management, dbt governance, data quality testing, CI/CD, infrastructure as code, cloud security and IAM best practices, and cross-cloud pipeline development. The role also requires maintaining dimensional models, SCDs, macros, and Airflow workflows.

Design, develop, and maintain data infrastructure, pipelines, ETL/ELT processes, data warehouses, models, and schemas. Transform and validate complex datasets, implement analytics solutions, and support data governance and quality. Collaborate with technical and non-technical stakeholders to translate business requirements into reliable data solutions. Document data processes and communicate findings across departments including Credit, Structuring, Operations, and Legal.

Design, build, and maintain unified data platforms using Microsoft Fabric and Azure services. Develop data pipelines, models, and analytics workloads with SQL, PySpark, and DAX. Integrate OneLake, ADLS Gen2, Synapse, Databricks, and Power BI while implementing medallion architecture, governance, lineage, CI/CD, and data-quality practices. Collaborate with stakeholders to deliver scalable solutions, troubleshoot workflows, optimize performance and costs, and meet service-level agreements.

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.

Design, build, and maintain scalable data pipelines and ETL solutions. Collaborate with teams to gather requirements, ensure data quality and integrity, optimize data workflows, and support analytics and decision-making across the organization.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

The Data Engineer is responsible for optimizing data architecture and pipelines, collaborating with teams to deliver data solutions, and building analytics tools for business insights.

The Software Engineer will manage data collection to support AI model training, operate cloud infrastructure, and collaborate with scientists on dataset roadmap.

Maintain and migrate MSSQL data pipelines and schemas to Snowflake and Google Cloud. Develop ETL processes, data models, and scalable solutions for analytics, reporting, and client products. Troubleshoot technical issues, support deployments and business users, participate in design reviews, and uphold development quality standards. Collaborate with data engineering and software teams using relational database, warehousing, and cloud technologies.

Design, build, and operate production ETL/ELT pipelines using Python and Pandas; author performant SQL for PostgreSQL and Trino; architect AWS data solutions (S3, Glue, Athena); implement Terraform-based IaC and automated CI/CD; embed security best practices; collaborate with product and analytics teams to deliver dimensional models, curated datasets, and Superset dashboards while working within Agile delivery.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

Own the data tier: design denormalized read models and snapshot tables, tune queries and indexes for low-latency reads, build ETL/CDC/delta refresh pipelines, publish freshness SLAs, implement PHI de-identification and synthetic data, deliver versioned migrations via Flyway, and build data quality and reconciliation checks. Act as escalation point and communicate data lineage and freshness to clinical stakeholders.

Build and maintain infrastructure and integrations that move CMS, SCADA, and other customer data into cloud platforms. Monitor and resolve connectivity issues, support AWS infrastructure, respond to data support tickets, collaborate with service delivery teams, and improve connectivity processes for scalability and reliability. The role requires Linux command-line expertise, knowledge of data protocols, networking, and IT security, plus flexibility to support global operations outside normal business hours.

Owner of company-wide reporting and analytics for Growth, Marketing, Product, Finance and Operations. Build and maintain dashboards, automated reports and data pipelines (dbt), ensure data quality and governance, run growth and product analyses (CAC, ROAS, LTV, retention), support A/B testing and ad-hoc decision support, leverage AI for automation, and coordinate VAs on data tasks.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

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.

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.