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Data Warehouse Analyst

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Onsite

Data Warehouse Analyst

Comunus Technologies

Data Warehouse Analyst

Skills

SQL QueriesData MiningTesting & QASnowflakeData AnalysisTalendPower BIAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Mumbai, Maharashtra, India

Work mode:

Onsite

Published on:

4 September 2026

What they need


Job Summary

We are looking for a Data Warehouse Analyst with 3+ years of experience in data analysis, SQL, data warehousing, ETL processes, data validation, and reporting. The candidate will work closely with business, application, ETL, and BI teams to understand data requirements, analyze source systems, validate data, and support the development and maintenance of enterprise data warehouse solutions. The role requires strong SQL, data analysis, data profiling, data mapping, data quality, and dimensional modeling skills. Knowledge of ETL tools and Power BI will be an added advantage. A modern data warehouse typically involves ingestion, transformation, modeling, and serving of data for analytics and reporting.

Key Responsibilities

  • Analyze business requirements and translate them into data and reporting requirements.
  • Understand source systems, databases, tables, relationships, and data flows.
  • Perform detailed data analysis, data profiling, and data validation.
  • Develop and maintain SQL queries for data extraction, analysis, reconciliation, and validation.
  • Analyze data discrepancies between source systems and data warehouse.
  • Prepare Source-to-Target Mapping (STTM) documents.
  • Understand and document data flow from source systems to staging, warehouse, and reporting layers.
  • Work with ETL developers to define transformation and business rules.
  • Validate ETL jobs and transformed data against business requirements.
  • Perform data reconciliation between source and target systems.
  • Identify data-quality issues such as missing, duplicate, incorrect, or inconsistent data.
  • Develop data-quality validation rules and assist in resolving data issues.
  • Support the design and maintenance of fact and dimension tables.
  • Understand dimensional modeling concepts including Star Schema and Snowflake Schema.
  • Analyze historical data and identify trends, patterns, and anomalies.
  • Prepare data extracts and datasets for reporting and BI teams.
  • Collaborate with Power BI/reporting teams to ensure accurate and reliable reporting data.
  • Support UAT, data validation, and production deployment activities.
  • Investigate production data issues and provide root-cause analysis.
  • Maintain technical and functional documentation related to data sources, mappings, transformations, and business rules.
  • Coordinate with Business Analysts, Data Engineers, Developers, QA teams, and business stakeholders.

Required Technical Skills

  • SQL - Mandatory
  • Strong hands-on experience with SQL.
  • Complex SELECT queries, joins, subqueries, CTEs, and aggregations.
  • GROUP BY, HAVING, CASE statements and analytical/window functions.
  • Views and stored procedures.
  • Data comparison and reconciliation queries.
  • Query performance optimization.
  • Experience with MS SQL Server is preferred.
  • Data Warehousing
  • Good understanding of:
  • Data Warehouse concepts
  • OLTP vs. OLAP
  • Staging / ODS / Data Warehouse / Data Mart
  • Fact and Dimension tables
  • Star Schema
  • Snowflake Schema
  • Primary and foreign keys
  • Surrogate keys
  • Slowly Changing Dimensions (SCD)
  • Historical data management
  • Data aggregation
  • Data lineage
  • Source-to-target mapping
  • Dimensional models commonly use fact and dimension tables, with star-schema designs optimized for analytical workloads.
  • ETL / Data Integration
  • Good understanding of ETL/ELT processes.
  • Experience working with ETL tools such as: Pentaho SSIS Informatica Talend Azure Data Factory
  • Ability to understand ETL workflows, transformation logic, dependencies, and scheduling.
  • Experience validating ETL jobs and identifying failed or incorrect data loads.
  • Data Quality Validation
  • Perform source-to-target data validation.
  • Validate record counts, totals, aggregates, and business rules.
  • Identify data anomalies and inconsistencies.
  • Perform duplicate and null-value checks.
  • Validate data transformations.
  • Perform reconciliation between source systems and warehouse.
  • Conduct root-cause analysis for data-quality issues.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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