Top Data Engineer Jobs in Hyderabad

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.

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 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.

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.

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.

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.

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

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.

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

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Design and implement distributed data systems for Apache Hudi and Onehouse’s managed data lakehouse. Responsibilities include developing concurrency controls, transactional capabilities, indexing schemes, metadata and data-access systems, and optimization algorithms for Kubernetes-based distributed processing. The role involves analyzing complex data, prototyping performance improvements, deploying solutions safely to production, and serving as a subject matter expert in databases, query engines, storage, and stream processing.

Build and productionize distributed data infrastructure for Onehouse’s managed lakehouse platform. Responsibilities include developing Apache Hudi transactional capabilities, concurrency control, indexing, metadata and data-access systems, and optimization algorithms for distributed processing on Kubernetes. The role involves analyzing complex data, prototyping performance improvements, collaborating with engineers, and safely deploying solutions. Candidates should bring strong object-oriented programming, distributed systems, algorithm, and relational database expertise.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

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