Top Data Engineer Jobs in Hyderabad

Build and manage data pipelines, enterprise connectors, raw-to-mart data layers, metadata and semantic indexes, vector databases, data quality controls, lineage, governance, and audit data stores. Support RAG and LLM applications with grounded, traceable data. Required skills include Python, SQL, modern data platforms, orchestration, API integration, testing, Git, and CI/CD. Bonus experience includes unstructured content processing, knowledge graphs, regulated life sciences data, and cloud or platform certifications.

Designs, builds, and maintains scalable ETL pipelines connecting Salesforce with Snowflake and other data systems. The role manages Salesforce APIs and bulk data loading, writes and optimizes SQL and Python for data transformation, conducts Git-based code reviews, and monitors pipeline performance, availability, and data integrity.

Designs and maintains data warehouses, metadata frameworks, dimensional models, and ETL pipelines using SnapLogic or Informatica, Python, dbt, Snowflake, and SQL. Optimizes data systems, supports reporting and dashboards, and implements data governance practices. Collaborates with cross-functional teams to ensure data quality, integrity, security, and compliance while exploring Generative AI applications in data engineering.

Build and optimize batch and real-time ETL/ELT pipelines using Python, PySpark, SQL, Snowflake, Databricks, and cloud platforms. Develop transformation and orchestration layers, implement data quality monitoring and alerting, optimize performance and cloud costs, and collaborate with architects, analysts, and data scientists. The role also requires data warehousing, dimensional modeling, CI/CD, version control, and documentation practices.

Design, develop, and manage scalable data solutions using Azure Cosmos DB and SQL. Build ETL pipelines, optimize database performance, integrate data systems, implement security practices, troubleshoot data issues, and document technical processes. Collaborate with application developers, data scientists, and cross-functional stakeholders to deliver data solutions. The role requires strong Azure data services, SQL, NoSQL, and programming experience, with Snowflake and cloud or big data knowledge preferred.

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.

Designs and executes ETL testing strategies, performs data reconciliation and integrity assessments, optimizes SQL queries, validates data across PostgreSQL, MSSQL, and Snowflake, and develops automated data quality testing suites to ensure reliable data pipelines and reporting.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Designs and maintains AWS data architectures, ETL/ELT pipelines, data warehouses, analytics datasets, dashboards, and visualization solutions. Uses SQL, Python, Spark, Glue, Lambda, S3, Athena, Redshift, and QuickSight to prepare, transform, validate, and govern data. Collaborates with product, engineering, and operations teams, optimizing performance, cost, scalability, and data quality while integrating data systems with AI and natural-language query tools.

Designs and maintains scalable Snowflake ETL/ELT pipelines, SnapLogic integrations, and dbt transformation models. Responsibilities include dimensional and medallion architecture, performance and cost optimization, data quality, governance, CI/CD, documentation, and responsible use of AI-assisted development tools. Partners with analytics teams, mentors junior engineers, and helps establish reusable AI workflows and engineering standards.

Own the administration, security, reliability, observability, and cost management of the company’s data platform. Operate and optimize Snowflake during migration from BigQuery, manage AWS S3 and vendor data ingestion, extend practices to Postgres and NoSQL databases, and implement access controls, SSO, encryption, monitoring, incident response, infrastructure as code, and CI/CD. Support machine learning and AI workloads, manage data vendors and tooling, improve cost transparency, and establish reusable data operations practices across teams.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Design, develop, and integrate SAP MDG solutions using ABAP, REST APIs, IDocs, and ETL. Collaborate with cross-functional teams in Agile/SCRUM, maintain code in Git, support testing and automation, and provide on-call support for master data systems.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Develops, maintains, monitors, troubleshoots, and optimizes Amazon Redshift data warehouses and ETL/ELT pipelines. Responsibilities include SQL and Python development, production support, performance tuning, data quality validation, Tableau support, incident resolution, and operational documentation. The role also supports migration from Redshift to Databricks using Spark, Delta Lake, PySpark, Workflows, and Unity Catalog.

Designs and maintains Salesforce Data Cloud solutions for audience segmentation, activation, real-time ingestion, and data governance. Responsibilities include developing SQL queries, configuring Data Extensions, Data Model Objects, Data Streams, Calculated Insights, and Computed Traits; architecting cloud integrations with Snowflake, AWS, Azure, or GCP; resolving data quality issues; and implementing consent and governance frameworks.

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.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Owns data architecture and scalable engineering systems for an AI-native finance platform. Responsibilities include canonical data modeling, configurable connectors, SQL/dbt transformation, warehouse optimization, orchestration, CDC, lineage, data quality, schema evolution, and audit-ready traceability. The role also supports curated data for ML and LLM systems, customer-hosted multi-cloud deployments, mentoring distributed engineers, and collaboration with AI, backend, product, and US-based leadership.

Design and develop scalable Azure ETL/ELT pipelines and modern cloud data platforms. Build Databricks and PySpark solutions, implement Bronze/Silver/Gold Medallion Architecture, develop Airflow and dbt workflows, create analytics data models, and maintain CI/CD, governance, and data quality standards.

Develop and optimize SQL queries, data models, ETL pipelines, and data ingestion processes using Microsoft Fabric, Azure, Databricks, and Snowflake. Build Data Lakehouse and Medallion Architecture solutions, manage full and incremental loads, and support data warehouse systems. Create dashboards with Power BI or Tableau, document workflows, and collaborate with data analysts and software engineers.

Design, develop, and maintain scalable data pipelines, ETL/ELT processes, integrations, and cloud-based data solutions. Optimize data storage, retrieval, workflows, and processing performance while ensuring data quality and reliability. Collaborate with cross-functional teams, troubleshoot complex engineering challenges, lead technical decisions, and mentor junior team members.

Build and deploy enterprise AI applications, data pipelines, and GenAI solutions using Python, LLMs, and RAG. Develop backend services and microservices integrating REST APIs, SQL, NoSQL databases, and enterprise data platforms. Optimize AI models for production environments while solving complex technical problems and collaborating effectively with stakeholders.

Designs, builds, and maintains enterprise data pipelines from Dynamics 365 F&O into ADLS Gen2, Azure Synapse, and Microsoft Fabric. Develops Spark/PySpark transformations, scalable data models, and curated analytics datasets while ensuring data quality, governance, lineage, and reliability. Analyzes AxDB and finance data structures, supports Dataverse integrations, and collaborates with Finance, Operations, and BI teams to deliver reporting and subscription-based analytics solutions.