Top SQL Jobs in Hyderabad

Develop, test, deploy, and support enterprise integration solutions using MuleSoft Anypoint Platform and Mule 4.x. Build System, Process, and Experience APIs; create REST/SOAP integrations; transform data with DataWeave; implement security, logging, monitoring, and error handling; write MUnit tests; troubleshoot production issues; and support CI/CD collaboration with technical and business teams.

Entry-level QA trainee responsible for creating and executing test cases, performing functional, regression, smoke, sanity, and integration testing, documenting defects, retesting fixes, and validating applications across browsers and devices. The role involves collaboration with developers and analysts, test documentation, and learning API, database, and automation testing using modern QA tools.

Design and architect enterprise Snowflake data platforms and solutions. Develop and administer Tableau dashboards, leverage Snowflake Cortex, and implement AWS-based data ingestion and integration using S3, Lambda, Glue, and IAM. Lead ETL/ELT frameworks, data modeling, data warehousing, SQL performance optimization, and enterprise data integration. Manage client and stakeholder relationships while delivering scalable, high-performing data architectures.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

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.

Customer-facing technical role deploying AI-enabled solutions into enterprise environments: prototype, integrate, configure, and deliver AI-native workflows, ETL pipelines, dashboards, and tooling to improve ESG reporting, automation, and operational efficiency.

The Program Manager will enhance Sprinto's customer journey by managing deployment projects, leading cross-functional initiatives, and improving customer lifecycle processes through data-driven insights and stakeholder management.

Manage global equity settlement operations across international markets, including trade matching, settlement exception resolution, cash and position reconciliations, and aged-fail remediation. Coordinate with custodians, counterparties, trading, finance, compliance, and engineering teams. Support SEC, FINRA, and global regulatory controls, reporting, audits, and process improvements. Use SQL, Metabase, and automation tools to improve operational efficiency and analytics.

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 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.

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.

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 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.

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.

Own and prioritize product backlogs, define user stories and acceptance criteria, lead Agile ceremonies, approve releases, and translate business needs into technical solutions. Analyze workflows, integrations, data flows, and enterprise platforms while aligning products with banking regulations. Use SQL, Power BI, and Tableau for data validation and dashboards, and govern UAT, QA scenarios, and defect resolution. Serve as a strategic advisor to executives and clients across media, pharma, and financial technology initiatives.

Design, build, and deploy AI/ML and Generative AI solutions for enterprise use cases. Build and maintain end-to-end ML pipelines, deploy models to production, and implement monitoring, observability, and model-drift detection. Work with APIs, vector databases, embeddings, and RAG-based applications, and collaborate with cross-functional teams to optimize scalable AI deployments.

Design, build, and optimize orchestrated data pipelines using dbt, Airflow, Azure Databricks, PySpark, SQL and Python. Implement CI/CD and automation, ensure data quality, perform ad-hoc analysis and reporting, collaborate with cross-functional teams, and troubleshoot DAG failures to support data-driven decision-making.