Top Data Engineer Jobs in Hyderabad

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 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.

Design, implement, and optimize graph data solutions for a supplier chain project. Responsibilities include graph modeling with LPG and RDF, Neo4j database development and query optimization, GCP data processing using BigQuery, GCS, and Dataproc, and integrating graph data with visualization tools. The role also involves schema and data design, SQL-based relational database work, performance tuning, and potentially ETL/ELT, streaming, API, and distributed systems development.

Build and operate a production data pipeline ingesting Indian MCA registration data. Responsibilities include raw-first S3 storage, resilient scraping and parsing, snapshot and diff processing, granular change events, scheduled batch jobs, alert digests, monitoring, anomaly detection, reference-table maintenance, and historical backfills. The role requires strong Python, PostgreSQL, testing, operational reliability, and production experience with uncooperative data sources.

Develop Azure data pipelines and Databricks Lakehouse solutions using Azure Data Factory, Azure SQL, SQL, and PySpark. Convert Informatica PowerCenter and Oracle PL/SQL code, curate and transform structured and unstructured data, implement data models and Delta Lake tables, and support SQL Server cloud migration. Use Azure services including ADLS and ADX, automate delivery through DevOps and CI/CD, estimate project effort, and lead complex development in an agile environment.

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.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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 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.

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.

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.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

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.

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.