Top Data Engineer Jobs in Hyderabad

Leads software and data engineering initiatives, designing, coding, testing, deploying, and optimizing enterprise-scale data platforms. Builds and modernizes data warehouses, lakes, and lakehouses using cloud, open-source, and ETL technologies. Troubleshoots distributed systems, improves performance and reliability, establishes engineering standards, and conducts code reviews. Contributes to GenAI and LLM-enabled data solutions, governance, security, and cross-functional Agile delivery while mentoring less experienced engineers.

Design and implement scalable data engineering solutions and EDW data models. Build ETL/ELT pipelines (IICS) into Snowflake, optimize performance, enforce governance, support production deployments, conduct code reviews, troubleshoot issues, and collaborate with cross-functional teams to deliver robust, maintainable data platforms.

Design and optimize scalable data pipelines, models, integrations, and engineering infrastructure. Ensure data quality, integrity, validation, and cleansing while collaborating with cross-functional teams to deliver business-focused data solutions. Enable advanced analytics and data-driven decision-making, improve operational efficiency, and adopt emerging data engineering technologies. The role also includes people leadership responsibilities, including recruiting, employee development, succession planning, retention, engagement, and fostering an inclusive culture.

Designs and delivers enterprise-scale Azure lakehouse platforms, data products, ETL/ELT and streaming pipelines using Databricks, Spark, Delta Lake, and related Azure services. Leads and mentors data engineers, establishes engineering standards, drives Agile delivery, and collaborates with technical and business stakeholders. Implements CI/CD, security, governance, monitoring, data quality, lineage, observability, performance tuning, and cost optimization for production data platforms.

Designs, develops, tests, tunes, and supports complex ETL solutions and data pipelines for data warehouses. Schedules pipelines with Airflow, manages cloud infrastructure, promotes code across environments, and builds CI/CD automation using GitHub Actions and Jenkins. The role also provides production support, applies modern SDLC practices, collaborates with cross-functional stakeholders, and communicates technical solutions effectively.

Design, build, and operate scalable cloud-native ETL/ELT pipelines and data platforms (Databricks, Snowflake) ensuring data quality, governance, performance, and enablement of analytics and AI/ML workloads. Partner with stakeholders, apply data modeling and transformations, modernize legacy ETL, follow CI/CD and secure engineering practices, and mentor junior engineers.

Design and maintain scalable data pipelines using Databricks, Airflow, Snowflake, and Python. Build REST APIs and data services on Azure, implement data quality and observability, optimize cloud architecture, and support reliable ELT processing. Collaborate with analytics, product, and platform teams, participate in architecture and code reviews, and troubleshoot complex production issues across data pipelines and cloud infrastructure.

Design and maintain scalable Databricks data pipelines, orchestrate workflows with Airflow, and build optimized Snowflake ELT solutions. Develop Python-based APIs and data services deployed on Azure, applying cloud security, scalability, monitoring, and cost-optimization practices. Implement data quality and observability controls, collaborate with analytics and platform teams, participate in architecture and code reviews, and resolve complex production issues across data infrastructure.

Designs and delivers scalable cloud-native data solutions using AWS, Apache Iceberg, dbt, Python, SQL, and Spark. Builds batch and near-real-time pipelines, ingestion and transformation frameworks, data quality controls, observability practices, and legacy modernization solutions. Supports schema evolution, partitioning, compaction, query optimization, architecture discussions, code reviews, and mentoring. Also contributes to information security management, risk assessments, continuity testing, security policies, and ISMS reporting.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, reliability, governance, and security while supporting data migration and deployment across systems. Collaborate with cross-functional teams, troubleshoot pipeline issues, optimize performance, document workflows, and guide junior team members. The role requires Databricks expertise and independent subject matter ownership.

Designs scalable data solutions, builds and maintains large-scale pipelines, models enterprise datasets, and enables analytics and AI/ML use cases on Palantir Foundry. The role develops Foundry applications and custom TypeScript frontends, integrates AWS and big-data technologies, applies distributed systems and microservices architecture, and supports DevOps, performance optimization, and agile delivery.

Develop and maintain scalable data pipelines, ETL processes, dashboards, workflows, and analytical tools. Design advanced SQL and Python solutions for large datasets, ensuring data quality, performance, security, and privacy. Lead technical design, review code and outputs, troubleshoot production issues, collaborate with stakeholders, establish development standards, and mentor junior developers. The role requires strong PL/SQL, SQL, Python, data modeling, BI, and automation expertise, with exposure to GCP tools considered beneficial.

Design, build, optimize, and deliver scalable cloud-based data pipelines and data products on GCP. The role requires hands-on development with BigQuery, Dataflow, Composer, Cloud SQL, DBT, Python, and SQL, plus expertise in data architecture, data quality, data contracts, and scalable cloud data solutions. Kubernetes knowledge is preferred.

Designs and maintains automated data pipelines integrating enterprise systems with Anaplan. Responsibilities include data transformation, validation, reconciliation, monitoring, troubleshooting, deployment support, documentation, governance, and production operations. The role partners with Anaplan architects, model builders, data teams, and business stakeholders to improve planning data reliability, quality, scalability, and automation across finance, supply chain, sales, and workforce planning processes.

Data Engineer role with no specific responsibilities, qualifications, technologies, salary, travel, or clearance requirements provided.

Design, build, and maintain data solutions and ETL pipelines with a focus on graph database technologies. Ensure data quality, optimize pipelines and graph queries, document workflows, troubleshoot production issues, and mentor junior team members.

Design, build, and maintain data pipelines and ETL processes; ensure data quality and governance; lead and mentor team members; collaborate with cross-functional teams to deliver scalable data solutions.

Design, build, and maintain scalable data solutions and ETL pipelines focused on graph database environments. Ensure data quality, optimize graph schemas and queries, troubleshoot pipelines, document processes, and mentor junior team members while collaborating with cross-functional teams.

Design, build, and maintain data solutions and ETL pipelines with emphasis on graph databases. Ensure data quality, optimize pipeline performance, troubleshoot complex issues, mentor team members, and coordinate cross-functional data engineering efforts.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, governance, and compliance while collaborating with multiple teams. Serve as a subject matter expert, make team decisions, solve cross-team data problems, and mentor junior data engineers.

Design, develop, and maintain data solutions and ETL pipelines on AWS. Ensure data quality and governance, build data models and warehouses, collaborate across teams, lead and mentor engineers, and drive data architecture and process improvements.

Lead delivery and operation of enterprise data platforms across multiple squads. Define roadmaps, engineering standards, semantic models, KPI definitions, data quality and lineage practices, AI-ready knowledge layers, cloud warehouse architectures, streaming pipelines, observability, FinOps, security, and compliance. Build high-performing teams, manage vendors, and ensure reliable, scalable delivery of data products supporting BI, analytics, machine learning, and generative AI.

Develop and maintain end-to-end data pipelines from transactional systems such as SAP and ServiceNow into Snowflake reporting environments and data lakes. Troubleshoot data jobs, connections, and pipelines; collaborate with global stakeholders and cross-functional teams; and work with Data Scientists and Data Engineers to optimize data solutions. The role also supports exploratory technology projects and new data services.

Design, build, and optimize scalable data lakehouse solutions on Azure using Python, PySpark, ADF, Databricks, and Function Apps. Develop reliable data pipelines and products, collaborate with product and marketing teams, follow coding standards and CI/CD practices, and continuously improve data workflows and performance.

MetLife is seeking a Big Data Engineer to support its AI-enabled, people-centered transformation within the financial services industry. The provided description does not include specific day-to-day responsibilities or qualifications.