Top SQL Jobs in Hyderabad

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.

Design and maintain automated tests using Python and Pytest; test REST APIs, databases, data pipelines, and Power BI dashboards. Validate data accuracy and reconciliation across source systems, APIs, databases, and reporting layers. Execute functional, regression, integration, and end-to-end testing, document defects, collaborate with development and data teams, and improve automation coverage and framework efficiency.

Early-career Associate Engineers will develop, test, debug, and maintain software and AI-first applications under experienced engineers. Responsibilities include applying programming, data structures, algorithms, SQL, APIs, Git, testing, and CI/CD practices while contributing to automation, proof-of-concepts, data, cloud, quality, full-stack, and AI/GenAI projects. The role emphasizes structured learning, collaboration, communication, problem-solving, and growth into specialized engineering disciplines.

Build and maintain a multi-tenant cloud case management platform across frontend, backend, APIs, databases, and AWS infrastructure. Develop Angular and React applications, Python services, REST integrations, PostgreSQL schemas and queries, operational dashboards, field workflows, and data migrations. Collaborate with technical and business teams to deliver tested features in an Agile/Scrum environment while using AI coding tools for development, debugging, reviews, and research.

Build and deploy production AI applications for enterprise Finance teams, from discovery with CFO stakeholders through prototyping, regulated deployment, adoption, and ROI measurement. Develop agentic and RAG workflows using Snowflake Cortex or Databricks Genie, LLM orchestration frameworks, vector search, and evaluation harnesses. Integrate governed enterprise data and platforms, ensure compliance and observability, and advise senior Finance leaders while creating reusable technical assets.

Develop and maintain Java Spring Boot microservices, build and consume REST APIs, write unit tests, participate in code reviews, and resolve production issues. Senior-level responsibilities include owning modules end-to-end, leading architecture discussions, mentoring junior developers, and optimizing performance. The role requires Java, Spring technologies, microservices, databases, version control, and build tools, with additional cloud, security, containerization, messaging, and CI/CD skills for senior positions.

Build AI-assisted browser automation and automated tests for web applications, APIs, and data workflows using Playwright, Stagehand, and Python. Integrate LLM APIs, CI/CD pipelines, monitoring, logging, metrics, and reporting dashboards. Develop reusable automation frameworks, evaluate deterministic versus AI-driven approaches, improve reliability, and optimize AI performance and costs.

Designs scalable Snowflake data models and architectures, builds ETL/ELT pipelines with Snowpipe, Azure Data Factory, and Matillion, and develops complex SQL and stored procedures. Responsibilities include incremental loading, data validation, performance tuning, warehouse and cost optimization, RBAC and governance, monitoring, error handling, troubleshooting, and production support. The role collaborates with cross-functional teams on requirements and solution design.

Lead the design, implementation, and optimization of Snowflake data warehousing solutions. Develop scalable data models and pipelines, optimize Snowflake performance and costs, oversee CI/CD automation, integrate monitoring and analytics tools, ensure data security and governance, and mentor junior engineers while aligning technical solutions with business requirements.

Develop and maintain Snowflake data pipelines, write and optimize SQL for integration and transformation, assist with CI/CD deployment automation, test and debug workflows, optimize performance, collaborate with senior developers, and document data processes and architectural decisions.

Design, develop, maintain, and optimize Talend ETL processes and scalable data pipelines. Extract and load data across databases, files, APIs, cloud platforms, and data warehouses such as Snowflake. Implement data transformations, quality checks, validations, monitoring, troubleshooting, documentation, governance, security, and privacy controls. Collaborate with architects, analysts, and stakeholders while supporting data migration and legacy-system integration projects.

Gather and analyze Utility/Energy business requirements, translate them into functional and technical specifications, and document BRDs, FRDs, user stories, and workflows. Collaborate with stakeholders, product owners, engineers, QA, and project managers on solution design, testing, UAT, deployment, and production support. Analyze utility processes involving metering, billing, customer information, outages, energy trading, settlements, and regulatory reporting while identifying improvement and automation opportunities.

Leads data analysis initiatives, reporting, dashboard and KPI development, data modeling, and analysis of large datasets. Translates business requirements into analytical solutions, partners with business and technical stakeholders, ensures data quality and governance, and mentors junior analysts to support data-driven decision-making.

Lead the design, development, and optimization of scalable GCP data pipelines, ETL/ELT workflows, BigQuery warehouses, and streaming solutions. Migrate on-premise systems to GCP, ensure data quality, security, governance, and reliability, and troubleshoot large-scale data platforms. Collaborate with technical and business stakeholders, establish data engineering best practices, and mentor junior engineers while guiding complex projects and recommending platform improvements.

Develop and maintain full-stack software across development, testing, and deployment. Design REST API backends, write SQL, optimize production code, enforce engineering best practices, and build reusable core components. The role requires independent decision-making, accountability, critical thinking, and strong communication while contributing to products serving immunization data and public health clients.

Build and deploy agentic finance AI solutions: design voice‑optimized prompts and conversational flows, integrate LLMs (Gemini/GPT/Claude) with RAG and enterprise data, implement voice/speech/telephony integrations, automate workflows and reporting using Python and SQL, and collaborate across product, engineering, and operations to tune and monitor production ML systems.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

Analyzes complex datasets, develops Tableau dashboards, performs statistical and predictive modeling using R and Python, and delivers actionable insights. Partners with cross-functional teams and business stakeholders to identify inefficiencies, support strategic decisions, and monitor solutions. Requires strong communication, problem-solving, and analytical skills.

Test AI-based conversational products through manual and automated testing. Develop and execute test plans, monitor quality assurance standards, assess data quality, identify model accuracy improvements, and provide detailed feedback to improve AI models. The role requires programming, API testing, SQL, CI/CD tools, functional and non-functional testing, and independent problem-solving.

Design, build, and operate Databricks-based data and AI platforms using Medallion Architecture, Spark, Delta Lake, and scalable ELT pipelines. Integrate enterprise systems, implement data quality and monitoring, enforce governance and security, manage cloud data lakes, and enable ML/AI workflows with MLflow. Maintain documentation, conduct code reviews, train stakeholders, and report progress against project milestones.

Design, develop, and deploy enterprise data pipelines and data products using AWS and Databricks. Build Medallion Architecture pipelines, write Python, PySpark, and SQL code, optimize data workflows, troubleshoot production issues, and implement CI/CD using Databricks Asset Bundles, GitHub Enterprise, and GitHub Actions. Work hands-on across the full data engineering lifecycle in an Agile/Scrum environment.

Develop, maintain, and support AS400/iSeries applications using RPG (ILE and free-form), ACOM, and ShowCase Query. Participate in design, testing, Agile delivery, production support, change control, and SOX/IT security compliance. Collaborate with business and technical teams to estimate work, triage defects, and deploy changes to production.

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 and maintain high-performance backend services in Rust using asynchronous programming, REST APIs, databases, Redis, messaging systems, Docker, Kubernetes, and AWS. Responsibilities include writing tested and maintainable code, participating in code reviews, troubleshooting production issues, optimizing performance, implementing monitoring and error handling, and collaborating with senior developers, QA, DevOps, and product teams.

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.