GCP Technical Specialist

Job Summary

Role Overview

The GCP Data Warehouse Analyst is responsible for analyzing, designing, validating, and supporting enterprise data warehouse solutions built on** Google Cloud Platform**, primarily using Big Query. The role focuses on data analysis, data quality, SQL development, data modeling support, migration validation, reporting enablement, and close collaboration with data engineering, analytics, and business teams.

This role requires strong hands-on experience in Big Query, SQL, data warehousing concepts, ETL/ELT processes, data validation, and BI/reporting support. Data Warehouse Analysts are typically expected to ensure that data is accurate, accessible, reliable, and aligned with business decision-making needs.

Key Responsibilities

Data Warehouse Analysis & Support

• Analyze business data requirements and translate them into data warehouse, reporting, and analytical needs.

• Support the design, development, and maintenance of data warehouse structures, data marts, and reporting layers.

• Work with data architects and data engineers to define source-to-target mappings, business rules, and transformation logic.

• Review data models, table structures, and metadata to ensure alignment with business requirements.

• Support migration, modernization, and validation activities involving legacy platforms and GCP Big Query.

2. Big Query & SQL Development

• Write, validate, and optimize complex SQL queries in Big Query for data analysis, reconciliation, reporting, and issue resolution.

• Use Big Query capabilities such as partitioning, clustering, materialized views, authorized views, UDFs, and time-series analysis where applicable.

• Analyze query performance and recommend improvements for cost optimization and faster execution.

• Validate data loads, transformation logic, and aggregated datasets across staging, curated, and consumption layers.

3. Data Quality, Validation & Reconciliation

• Perform data profiling to identify anomalies, duplicates, missing values, inconsistencies, and data quality issues.

• Conduct reconciliation between source systems and Big Query tables to confirm completeness and accuracy.

• Create and execute validation scripts for migration, ingestion, and transformation workflows.

• Partner with business users to validate metrics, KPIs, and reporting outputs.

• Support root-cause analysis for data discrepancies and coordinate defect resolution with engineering teams.

4. ETL / ELT & Pipeline Collaboration

• Work with data engineering teams to understand and validate ETL/ELT pipelines on GCP.

• Support data ingestion from source systems such as relational databases, APIs, files, SaaS platforms, and cloud storage.

• Review and validate pipeline outputs generated through GCP tools such as Dataflow, Dataproc, Data Fusion, Big Query scheduled queries, and Cloud Composer/Airflow.

• Interpret workflow orchestration logic and assist in tracing data pipeline failures or delays.

5. Reporting & Business Intelligence Enablement

• Support reporting and dashboard requirements using BI tools such as Looker, Looker Studio, Tableau, or Power BI.

• Create analytical datasets, views, and extracts for business reporting and self-service analytics.

• Collaborate with business analysts and reporting teams to ensure metrics are clearly defined and consistently calculated.

• Provide data explanations, ad-hoc analysis, and insights to business stakeholders.

6. Documentation & Governance

• Document data definitions, lineage, source-to-target mappings, validation rules, transformation logic, and known data issues.

• Support metadata management, data cataloging, and data governance processes.

• Ensure data handling aligns with enterprise security, privacy, and compliance standards.

• Maintain clear documentation for recurring analytical processes and operational support.

Skill Requirements

Technical Skills

• Strong hands-on experience with Google Big Query.

• Advanced SQL skills, including joins, CTEs, window functions, aggregations, performance tuning, and query optimization.

• Good understanding of data warehouse concepts, dimensional modeling, star/snowflake schemas, fact and dimension tables.

• Experience with ETL/ELT concepts and data pipeline validation.

• Familiarity with GCP data ecosystem, including:

• Big Query

• Cloud Storage

• Cloud Composer / Airflow

• Dataflow

• Dataproc

• Data Fusion

• Pub/Sub

• Dataplex / Data Catalog

• Experience with BI/reporting tools such as Looker, Looker Studio, Tableau, or Power BI.

• Working knowledge of Python for data analysis, automation, and validation is desirable; Python/dataframe skills are commonly expected in GCP analytics roles. skills.google

Functional / Analytical Skills

• Strong data analysis, profiling, reconciliation, and validation skills.

• Ability to understand business KPIs and translate them into data requirements.

• Strong problem-solving skills for investigating data issues and pipeline discrepancies.

• Ability to work with large datasets and identify trends, outliers, and anomalies.

• Strong documentation and communication skills.

Other Requirements

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, Mathematics, Statistics, or a related field.

• Typically 7–10 years of experience in data warehousing, data analysis, data validation, business intelligence, or analytics engineering.

• At least 4**–6 years of hands-on experience with GCP data services**, preferably Big Query.

• Google Cloud certification such as:

• Google Professional Data Engineer

• Google Cloud Digital Leader

• Big Query / Data Analytics-related certification

• Experience in cloud data warehouse migration projects, especially from Oracle, SQL Server, Teradata, Hadoop, or Snowflake to Big Query.

• Exposure to dbt or** Dataform** for analytics engineering and transformation management.

• Experience with data governance tools such as Dataplex or Data Catalog.

• Knowledge of data privacy and compliance requirements such as HIPAA, GDPR, SOC2, or PCI, depending on industry context.

Exposure to Big Query ML, Vertex AI, or anomaly detection frameworks is a plus.