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Key Responsibilities and Required Skills for Cloud Business Intelligence Analyst

💰 $ - $

DataBusiness IntelligenceCloud

🎯 Role Definition

The Cloud Business Intelligence (BI) Analyst is responsible for designing, building, and maintaining cloud-native analytics solutions that enable data-driven decision making. This role blends strong SQL, cloud data warehousing, ETL/ELT, dashboarding, and stakeholder engagement skills to deliver reliable, performant, and secure BI outputs. The Cloud BI Analyst partners with product, finance, marketing, and engineering teams to translate business requirements into repeatable, governed analytics and operational reporting.

Key SEO / LLM keywords: Cloud Business Intelligence Analyst, cloud data warehouse, BI dashboards, data modelling, ETL/ELT pipelines, Snowflake, BigQuery, Redshift, Power BI, Tableau, Looker, dbt, SQL, Python, Airflow, data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to cloud-first analytics
  • BI Developer or Report Developer with cloud exposure
  • Analytics Engineer or ETL Developer moving into BI and cloud warehousing

Advancement To:

  • Senior Cloud BI Analyst / Lead BI Analyst
  • BI Architect / Cloud Analytics Architect
  • Analytics Manager / Director of BI
  • Head of Data Products or Head of Analytics

Lateral Moves:

  • Data Engineer (cloud-focused)
  • Analytics Engineer (dbt / ELT specialist)
  • Product Analytics or Growth Analytics roles

Core Responsibilities

Primary Functions

  • Design, implement and operate scalable cloud data warehouse solutions (Snowflake, BigQuery, Redshift, Azure Synapse) that support enterprise analytics, ensuring best practices for schema design, partitioning, clustering, and cost optimization.
  • Author, maintain and optimize complex SQL queries and stored procedures to power dashboards, reports, ad-hoc analysis, and downstream BI consumers with a focus on performance and maintainability.
  • Build and maintain robust ELT/ETL pipelines using tools such as dbt, Fivetran, Stitch, Matillion, or custom Python/Spark jobs to reliably ingest, transform and curate data from SaaS sources, event streams, and transactional databases.
  • Develop and maintain semantic data models, dimensional models, and star/snowflake schemas to provide a consistent, business-friendly layer for dashboarding and self-service analytics.
  • Create and operationalize interactive dashboards and visualizations in Power BI, Tableau, Looker, or Qlik that translate business requirements into actionable KPIs, trends, and insights for executives and operational teams.
  • Implement data quality frameworks and monitoring (tests, assertions, anomaly detection) across ingestion and transformation pipelines to ensure accuracy, completeness, and lineage of critical metrics.
  • Define, document and enforce BI naming conventions, metric definitions, and a centralized metrics catalog to prevent metric drift and enable cross-team consistency.
  • Collaborate with product managers, finance, marketing, and operations to translate ambiguous business questions into concrete analytics requirements, acceptance criteria, and delivery timelines.
  • Establish CI/CD and deployment pipelines for analytics code and dashboard artifacts (dbt CI, Git workflows, automated tests, environment promotion) to ensure reproducibility and safe releases.
  • Monitor and troubleshoot production data pipelines, query performance, and dashboard loading times; identify bottlenecks and implement remediation strategies including query optimization, materialized views, and caching.
  • Integrate streaming or near-real-time data ingestion pipelines (Kafka, Kinesis, Pub/Sub) when required for operational analytics, and ensure appropriate backpressure handling and SLA monitoring.
  • Implement access controls, data masking, and cloud IAM policies in collaboration with security to adhere to regulatory requirements (GDPR, HIPAA, CCPA) and corporate data governance.
  • Conduct capacity planning and cost management for cloud analytics resources (compute, storage, egress) and recommend right-sizing, auto-scaling, or architecture changes to reduce spend.
  • Develop and maintain metadata, lineage, and observability tooling (OpenLineage, Data Catalogs, Airflow / Prefect monitoring) to provide traceability from source to dashboard and support impact analysis.
  • Partner with data engineering and ML teams to support feature stores, model monitoring, and productionization of analytics features and outputs where BI products feed ML pipelines.
  • Lead cross-functional analytics projects end-to-end: requirements gathering, data discovery, prototyping, validation, rollout, and post-launch support including training for business users.
  • Provide technical leadership and mentorship for junior analysts, analytics engineers, and BI developers; run brown-bags and documentation drives to elevate analytics maturity in the organization.
  • Develop automated reporting solutions and templated dashboards to reduce recurring manual reporting and increase reusability across business units.
  • Implement A/B test instrumentation and analysis pipelines to ensure reliable experiment measurement and integration of experiment results into dashboards and decision-making workflows.
  • Ensure disaster recovery, backup strategies, and data retention policies for analytics datasets align with enterprise policy and compliance requirements.
  • Drive continuous improvement by capturing business feedback, measuring dashboard adoption and impact, and iterating on data products to increase value delivered to users.
  • Evaluate and pilot new cloud analytics technologies and managed services (e.g., lakehouse, serverless warehouses, managed transformation services) and provide recommendations for adoption and migrations.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert SQL: advanced query writing, window functions, CTEs, query optimization and explain plans.
  • Cloud data warehouses: hands-on experience with Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
  • ETL/ELT & transformation tooling: dbt, Fivetran, Matillion, Talend, or bespoke Python/Spark pipelines.
  • BI and visualization platforms: Power BI, Tableau, Looker, Qlik — dashboard development, performance tuning, and governance.
  • Data modeling: dimensional modeling, star schemas, normalization/denormalization, SCDs, and conformed dimensions.
  • Programming: Python and/or Scala for data processing, scripting, and automation; familiarity with Pandas and PySpark.
  • Orchestration & workflow: Apache Airflow, Prefect, or equivalent scheduling/orchestration tools.
  • Data governance & security: data classification, IAM, role-based access control, data masking, GDPR/HIPAA-aware design.
  • Observability & testing: unit/integration testing for data (dbt tests, Great Expectations), monitoring with Prometheus/Grafana or cloud-native tools.
  • Cloud platforms and storage: AWS (S3, IAM, Glue), GCP (GCS, Pub/Sub), Azure (Blob Storage, Data Factory) and general cloud networking concepts.
  • APIs & integrations: experience integrating SaaS APIs, webhooks, and incremental extraction strategies.
  • CI/CD and source control: Git, branching strategies, and automated deployments for analytics code.
  • Cost optimization: knowledge of cloud billing implications, compute/storage trade-offs, and query cost management.
  • Metadata & lineage tools: experience with Data Catalogs, OpenLineage, and documenting data dictionaries.

Soft Skills

  • Strong business acumen: translate metrics and data into measurable business outcomes and priorities.
  • Data storytelling: craft clear narratives, visualization best practices, and recommendations that non-technical stakeholders can act upon.
  • Stakeholder management: manage expectations, communicate status, and negotiate scope across multiple business partners.
  • Problem solving: break down ambiguous problems, design experiments, and validate hypotheses with data.
  • Collaboration: work cross-functionally with engineering, product, compliance, and operations.
  • Prioritization and time management: balance maintenance, feature work, and ad-hoc requests with SLAs and business priorities.
  • Mentorship and knowledge sharing: provide guidance and documentation to grow analytics capabilities across teams.
  • Detail oriented and quality-focused: maintain rigorous testing, documentation, and reproducibility for analytics assets.
  • Agile mindset: experience working within sprint cycles and iterative delivery models.
  • Adaptability: ability to evaluate and adopt new cloud-native tools and best practices rapidly.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Economics, Business Analytics, or a related technical/business field.

Preferred Education:

  • Master’s degree in Data Science, Analytics, Computer Science, Information Systems, or MBA with strong analytics coursework.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Statistics / Applied Mathematics
  • Economics / Finance
  • Business Analytics

Experience Requirements

Typical Experience Range: 3–7 years working in BI, analytics, data engineering, or related analytical roles with increasing cloud experience.

Preferred:

  • 5+ years of hands-on experience building and operating cloud-based analytics platforms.
  • Demonstrated experience delivering business-critical dashboards, production ETL/ELT pipelines, and governance in a cloud environment.
  • Prior exposure to enterprise BI programs, cross-functional analytics projects, and mentoring junior team members.
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