Top SQL Jobs in Hyderabad

Build and ship AI-powered features for fashion, beauty, and home using large-scale product, image, and consumer data. Prototype with LLMs, agents, multimodal and CV models, run experiments, measure impact, and take models end-to-end from hypothesis to production while collaborating with product, design, and business teams.

Develop and execute monetization models, pilot revenue initiatives, analyze conversion and market data, manage budgets, identify business partnerships, and collaborate on growth plans. The role turns data into actionable business insights, communicates findings to stakeholders, and supports strategic decisions that drive revenue and expansion.

Join a seven-person global core team building a cross-platform Digital Concierge mobile application. Depending on the role, responsibilities span product requirements, project delivery, UI/UX, Flutter development, backend APIs, QA, or DevOps. Team members will contribute across the full product lifecycle, collaborate remotely across time zones, solve problems independently, and deliver production-ready features. Applicants must have no more than two years of professional experience and demonstrate ownership, adaptability, communication, and the ability to learn quickly.

Own customer relationships from onboarding through adoption and retention. Diagnose technical issues using SQL, logs, APIs, and system diagnostics; interpret dashboards and customer data; deploy AI tools to improve support workflows; and collaborate with Product and Engineering on escalations. The role also includes reporting, process optimization, knowledge-base development, SLA adherence, and global customer interactions in a 24x7 environment.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Develop and maintain end-to-end web applications using modern JavaScript and TypeScript frameworks, Node.js, APIs, databases, and cloud platforms. Design responsive interfaces, implement secure authentication, write documentation and automated tests, debug performance and reliability issues, and follow Agile development practices. Collaborate with designers, product managers, testers, and other stakeholders to translate requirements into technical solutions. Mentor junior developers, recommend innovative improvements, and continuously optimize application code.

Own enterprise supply chain implementations from discovery through go-live, translating complex planning and execution challenges into AI-native models and workflows. Build and debug Python, SQL, optimization, simulation, and machine learning solutions on Lyric’s platform. Partner with customers, Product, and Engineering; mentor junior engineers; develop reusable accelerators; and apply AI tools to improve delivery. Focus areas include network optimization, scenario analysis, demand planning, inventory management, production scheduling, and supply chain execution.

Analyze large-scale datasets to generate actionable business insights, define and monitor KPIs, build dashboards and automated reports, maintain data pipelines, and investigate anomalies across ad delivery, bidding, monetization, campaigns, traffic quality, and platform performance. Collaborate with product, engineering, operations, and business teams to support strategic and real-time decisions.

Own the technical infrastructure supporting a large-scale camera and sensor data ingest pipeline. Build and maintain ingest stations, tooling, hardware fleets, quality controls, media lifecycle standards, inventory processes, and reporting. Troubleshoot failures, ensure data integrity and chain of custody, automate workflows with Python and shell scripts, train operators, document procedures, and support field deployments.

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.

Design and maintain ETL/ELT pipelines and data warehouse architecture, analyze and visualize data with SQL/Python/R and BI tools, build machine learning models and A/B tests, and partner with engineering and business teams to deliver data-driven insights for fintech use cases (fraud detection, segmentation, forecasting).

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.

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.

Build and run end-to-end GTM experiments: define ICPs and segments, research accounts, design and launch ABM/outbound campaigns, automate workflows connecting CRM, enrichment and AI systems, and tie campaign activity to pipeline and revenue while iterating based on results.

Leads and scales a global customer support function for a non-custodial crypto wallet. Responsibilities include managing and developing support staff, handling technical escalations, improving workflows and knowledge systems, monitoring service metrics, and enhancing AI-powered support. The role partners with Product, Engineering, Operations, and the AI Platform team on quality evaluation, safe human escalation, automation, rollout readiness, and user experience improvements.

Designs and implements scalable Zoho business solutions for clients across operational industries. Responsibilities include configuring workflows and automation, developing custom Deluge and API integrations, translating business requirements into system designs, supporting implementation and deployment, documenting solutions, and training users. The role covers processes such as inventory, procurement, production, logistics, field operations, fleet management, and customer service.

Build and operate governed MLOps and LLMOps platforms on Databricks. Productionize ML and GenAI models, RAG pipelines, vector search, and inference services; implement CI/CD, data quality, security, lineage, observability, incident response, disaster recovery, and cost controls. Partner with data science, engineering, platform, product, and security teams to ensure reliable, secure, compliant, and cost-effective AI workloads across development, QA, and production environments.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.