Top Data Engineer Jobs in Hyderabad

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.

Design, build, and optimize scalable ETL/ELT pipelines and enterprise data platforms on Azure and Snowflake. Improve Snowflake data models and performance, collaborate with cross-functional Agile teams, and support data warehousing, big data, and cloud analytics solutions.

Senior Azure Data Engineer responsible for designing and implementing end-to-end data pipelines using Azure Databricks, Azure Data Factory, and PySpark. Work with Delta Lake, Spark SQL, and data warehousing concepts to build scalable ETL/ELT solutions; collaborate on analytics and Power BI integration. Availability for 2pm-11pm/12am IST shift required.

Design, build, and maintain scalable data pipelines and architectures using cloud and streaming technologies. Develop ETL/streaming workflows, optimize performance and reliability, integrate multiple data sources, ensure data governance and security, and collaborate with analytics and product teams to support data needs.

Design, build, and maintain scalable data pipelines and models using Big Data and cloud technologies. Ensure data quality, security, SLA adherence, and collaborate with analytics and ML teams. Monitor, optimize, and document ETL/ELT processes.

Design and build scalable batch and real-time data pipelines using Spark, Kafka, Java and SQL. Develop ETL/ELT frameworks, streaming solutions, data models and cloud data warehouses. Automate workflows with Airflow/DBT, monitor reliability, collaborate with Data Science and Engineering, and follow governance, security, and cost-optimization best practices.

Design, build, and maintain scalable ETL/ELT data pipelines and data models. Orchestrate workflows with Apache Airflow, implement transformations with dbt, use BigQuery and GCS, ensure data quality and performance, and collaborate with analytics teams to support BI and analytics initiatives.

Build and maintain large-scale data architectures and pipelines using AWS and big-data technologies. Develop ETL/streaming solutions, design data lakes, support analytics and ML use cases, implement data governance, optimize queries, and collaborate with product and data stakeholders to deliver scalable, reliable data platforms.

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 Sr GCP Data Engineer will work with cloud technologies to engineer data solutions, requiring over 5 years of experience in the field.

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.

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

The Data Engineer will design and maintain data pipelines, build data warehouses, and ensure data quality while collaborating with data scientists and engineers.

Serve as a technical product-facing data engineer translating product requirements into build-ready engineering artifacts. Partner with product, architecture, and platform teams to design and validate data models, SQL queries, pipelines, APIs, and data quality checks. Contribute hands-on to schema definitions, metadata, pipeline validation, automation, and observability to enable reliable cloud data platform delivery and accelerate engineering execution.

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.

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.

Build and prepare data pipelines: mapping, cleansing, transformation, integration, and wrangling using Python and SQL. Perform feature engineering for structured and unstructured data and support API and web app deployment for data products.

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.

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.