Top Data Analyst Jobs in Hyderabad

Design, build, and operate Azure/Databricks Lakehouse solutions including batch and streaming ingestion, storage and dimensional modeling, data transformation and quality, Power BI semantic layers and reporting, observability and security, and cost optimization across data pipelines.

The Associate Manager Data Analyst will design and optimize data analytics programs and pipelines, ensure data governance, and mentor junior analysts while maintaining best practices in ETL processes and using various technologies for data engineering tasks.

The Data Governance Analyst supports Global Procurement, ensuring trusted data governance, improving master data quality, and partnering cross-functionally to meet reporting needs and enhance decision-making processes.

Lead design and execution of data quality validation, profiling, monitoring and reporting for production data pipelines. Build/maintain DQ rules and artefacts, perform testing across lifecycle stages, detect anomalies, collaborate with business and engineering partners, and mentor junior DQ analysts.

Design, operate, and govern an Azure + Databricks lakehouse across ingestion, storage, transformation, and serving. Build batch and streaming pipelines with CI/CD, automated data quality, observability, and security controls. Model curated datasets and semantic layers for Power BI, manage PostgreSQL replication and CDC, optimize costs, and collaborate with product owners and data stewards to certify enterprise metrics.

Lead marketing measurement and analytics: design and evaluate A/B, holdout and geo tests; analyze campaign and channel performance; build causal insights and executive-ready recommendations; collaborate cross-functionally to improve measurement frameworks and reporting.

Ensure production data quality across the analytics lifecycle by building validation rules and test cases, performing profiling and DQ checks across testing stages, detecting anomalies, maintaining DQ artefacts, monitoring pipelines, and collaborating with business, technology, operations, and D&A teams.

Design, build, and maintain ETL/data pipelines using Sparkola, PySpark, and SQL. Validate and reconcile datasets for downstream analytics (QlikSense, Superset). Use CML for development, manage jobs via Sparkola Job Server, and liaise with business stakeholders while following governance, JIRA, and change-control processes.

Design, develop, and maintain interactive Power BI/Fabric dashboards, conduct data analysis to surface trends and insights, collaborate with stakeholders to gather requirements, ensure data quality through ETL and modeling, and present findings to inform business decisions.

Design, build, and operate Azure/Databricks lakehouse pipelines (batch and streaming) with governance, data quality, observability, and cost controls. Model curated datasets and semantic layers in Power BI, manage PostgreSQL replication/CDC to lakehouse/Synapse, enforce security/compliance, and collaborate with product owners and data stewards to certify enterprise datasets.

Design and operate an Azure + Databricks lakehouse; build batch and streaming ingestion pipelines with CDC, automated data quality, CI/CD and observability; model curated datasets and semantic layers for Power BI; enforce security/compliance controls and cost optimization; collaborate with product owners and data stewards.

The Supply Chain Data Analyst analyzes complex data sets, monitors inventory levels, develops reports, and collaborates with stakeholders to drive business decisions.

Design, build, and maintain scalable batch and streaming data pipelines; ingest, transform, cleanse, validate, and store data from databases, APIs, files and streams. Define data models (star, snowflake, Data Vault), manage schema evolution and partitions, and implement reusable, efficient code using Azure, Spark, Airflow/Glue/ADF, Kafka/Kinesis, DBT, Python, and advanced SQL.

The Software Engineer - Data Analyst / Developer will support the BI team by analyzing business needs, developing reports, dashboards in Tableau, ensuring data quality, and providing user support.

Analyze complex supply chain data to improve performance, monitor inventory, develop reports, and support key initiatives in collaboration with stakeholders.

The Senior Reinsurance Data Analyst optimizes reinsurance processes by leveraging data analysis, AI tools, and process improvements while ensuring accuracy and efficiency.

The Data Analyst role involves data analysis and reporting, supporting decision-making processes through data insights.

Analyze and organize reliability data for fixed and non-fixed industrial assets, assess failure probability and consequence, develop reliability strategies, gather operations and maintenance data, validate findings with customers, and deliver recommendations using database systems and industry standards.

Responsible for data extraction, collation, and analysis in asset management; requires communication with investment professionals; ensures data quality and processes for information collection.

Review AI-extracted transcript data against source documents, correct errors, validate required fields, follow workflows and escalation procedures, maintain queue productivity and quality, and document issues for team leads.

Designs, develops, tests and maintains database queries, ETL pipelines (SSIS) and reports (SSRS). Performs data transformation, tuning and monitoring, builds Power BI visualizations, integrates cloud AI services, and communicates insights to stakeholders to improve data-driven decisions.

The Data Analyst will analyze and process data from Private Equity firms, ensuring quality control of financial documents and reporting. Responsibilities include data categorization, research, and resolving issues related to cash flow entries and capital transactions.

As a Data Analyst, you'll convert financial documents into editable files, format data, ensure quality, report daily work, and respond to team communications.

Design, build, and maintain cross-vendor market data pipelines and QA processes. Investigate complex data issues, handle escalations, collaborate with product and engineering via JIRA, and ensure data accuracy for equities fundamentals, estimates, and market data.

Lead design and execution of data governance policies, metadata management, lineage, and stewardship. Develop standards, tools, and training; manage governance platforms (e.g., Collibra); identify Critical Data Elements; monitor metrics and SLAs; resolve governance issues; collaborate across Business, Technology, and D&A teams; and provide people leadership to coach and develop data governance talent.