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Key Responsibilities and Required Skills for Database Analyst

💰 $65,000 - $120,000

DatabaseData AnalysisSQLBusiness IntelligenceData Engineering

🎯 Role Definition

The Database Analyst is responsible for designing, developing, optimizing and maintaining relational and analytical databases to ensure reliable, performant access to data across the organization. This role blends strong SQL and data modeling skills with practical knowledge of ETL, performance tuning, backup/recovery and collaboration with business intelligence and engineering teams to translate business requirements into robust data solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Database Analyst or Data Analyst with strong SQL and reporting experience
  • Database Administrator (Junior DBA) transitioning to analytics-focused responsibilities
  • ETL Developer or BI Developer moving into database-centric analysis and architecture

Advancement To:

  • Senior Database Analyst or Lead Database Analyst
  • Database Administrator / DBA Lead
  • Data Architect or Enterprise Data Architect
  • Business Intelligence Manager / Analytics Manager

Lateral Moves:

  • Data Engineer (ETL / pipeline development)
  • BI Developer / Report Developer
  • Data Quality or Data Governance Specialist

Core Responsibilities

Primary Functions

  • Design, build and maintain complex SQL queries, stored procedures, functions and views to support operational systems and business reporting, ensuring clarity, maintainability and performance for both OLTP and OLAP workloads.
  • Perform in-depth database performance tuning and optimization, including query plan analysis, index strategy design (clustered/non-clustered), partitioning, statistics maintenance, and elimination of blocking or long-running transactions to meet SLA targets.
  • Develop and maintain normalized and dimensional data models (3NF, star/snowflake schemas), collaborating with business stakeholders and data architects to ensure models accurately represent business entities and reporting requirements.
  • Lead ETL design and development using tools such as SSIS, Informatica, Talend, AWS Glue, or custom Python scripts; implement reliable, idempotent batch and streaming data pipelines with robust error handling and monitoring.
  • Implement and enforce data quality rules, validation checks, and reconciliation processes to guarantee data integrity across source systems, staging areas, and production data warehouses.
  • Plan, execute and document backup, restore and disaster recovery procedures for critical databases, including point-in-time recovery strategies, backup schedules, and periodic restore testing.
  • Monitor and manage database capacity planning, growth forecasting, and resource utilization to proactively scale storage, CPU, memory and I/O performance in physical and cloud environments.
  • Collaborate closely with business analysts, product owners and BI teams to translate requirements into data models, metrics definitions, ETL specifications and production-ready reporting datasets.
  • Build and maintain high-quality, production-grade reporting datasets, materialized views, and aggregates to accelerate business intelligence dashboards and ad hoc analytics.
  • Design and implement security and access controls at the database and object level, including role-based access, row-level security, encryption-at-rest and in-transit, and auditing in compliance with regulatory requirements (e.g., GDPR, HIPAA).
  • Troubleshoot and resolve database incidents, outages, and critical performance degradations in production with timely RCA (root cause analysis) and corrective actions.
  • Create and maintain technical documentation for database schema, ETL flows, operational runbooks, maintenance windows and deployment procedures to support knowledge sharing and on-call rotations.
  • Automate routine DBA and analyst tasks (index maintenance, stats updates, deployment scripts, alerts) using scripting languages such as Python, PowerShell, or Bash and CI/CD tooling.
  • Collaborate with application developers to review and optimize application-generated SQL, implement connection pooling best practices, and reduce transaction contention to improve end-to-end performance.
  • Participate in database upgrade, migration and consolidation projects (on-prem ➜ cloud migrations like AWS RDS/Aurora, Azure SQL, Google Cloud SQL), including schema conversions, compatibility testing and cutover planning.
  • Implement monitoring, observability and alerting for database health using tools like New Relic, Datadog, Prometheus, SQL Sentry, or native cloud monitoring to detect anomalies and SLA violations.
  • Perform schema design reviews and data governance enforcement to standardize naming conventions, metadata management and lineage for downstream analytics and reporting.
  • Optimize ETL and reporting workloads for cost efficiency and performance when running in cloud platforms by leveraging best practices (serverless functions, caching, materialized views, compute scaling).
  • Conduct capacity and performance testing of database changes, new features and query patterns in pre-production environments to avoid regressions in production.
  • Support cross-functional data migrations, integrations and master data management efforts by mapping schemas, designing transformation logic and validating migrated data for completeness and consistency.
  • Lead or participate in data steward and governance councils to define data ownership, quality KPIs and lifecycle policies for critical business domains.
  • Mentor junior analysts and DBAs on best practices for SQL development, performance tuning, data modeling and production operations to raise team capability.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis, delivering timely, well-documented datasets and SQL-based insights to stakeholders across finance, operations, sales and product teams.
  • Contribute to the organization's data strategy and roadmap by identifying technical debt, proposing modernizations (cloud, automation, data catalog), and estimating implementation impacts.
  • Collaborate with business units to translate data needs into engineering requirements, acceptance criteria and data contracts that support repeatable analytic workflows.
  • Participate in sprint planning and agile ceremonies within the data engineering team, providing estimates, accepting tasks, and ensuring deliverables meet quality and performance requirements.
  • Assist in vendor evaluations and POC activities for database platforms, managed services, analytics tools and ETL technologies, providing technical criteria and migration risk assessments.
  • Provide training sessions and documentation for self-service analysts on SQL best practices, using curated data models and governed reporting datasets to improve analytics consistency.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-level SQL: advanced SELECT, JOINs, window functions, CTEs, subqueries, query rewriting, and query plan analysis.
  • Database design and data modeling: strong experience with 3NF and dimensional modeling (star/snowflake), normalization/denormalization trade-offs.
  • Performance tuning and query optimization: index strategy, execution plans, statistics management, partitioning and optimizer hints.
  • ETL development and orchestration: hands-on with SSIS, Informatica, Talend, Fivetran, Stitch, Airflow or equivalent; designing fault-tolerant pipelines.
  • Stored procedures, functions and trigger development with a focus on maintainability and performance.
  • Relational database platforms: Microsoft SQL Server, Oracle, MySQL, PostgreSQL (experience with at least one major RDBMS).
  • Cloud database technologies: AWS RDS/Aurora, Azure SQL Database, Google Cloud SQL, Redshift or Snowflake for analytical workloads.
  • Scripting and automation: Python, PowerShell or Bash for maintenance scripts, automation, and lightweight ETL tasks.
  • Backup/recovery and high availability: transaction log backups, clustering, Always On availability groups, replication and failover planning.
  • Data warehousing and BI tooling: familiarity with data warehouse patterns, OLAP vs OLTP distinction, and BI tools such as Power BI, Tableau, Qlik.
  • Monitoring and observability tools: experience with Prometheus, Datadog, New Relic, SQL Sentry, or native cloud monitoring for DB metrics and alerts.
  • Security and compliance: implementing RBAC, encryption, auditing and working knowledge of GDPR, HIPAA, PCI or industry-specific regulations.
  • Data quality and profiling: use of tools or SQL-based methodologies for data validation, anomaly detection and automated reconciliation.
  • Indexing strategies and physical database design including partitioning, compression and storage optimization.
  • Familiarity with NoSQL and analytical engines (MongoDB, Cassandra, BigQuery) is a plus for hybrid data architectures.

Soft Skills

  • Strong analytical mindset with a focus on translating ambiguous business questions into formal data requirements and technical solutions.
  • Excellent verbal and written communication skills for cross-functional collaboration, documentation and stakeholder presentations.
  • Detail-oriented and quality-driven — produces well-documented, tested and maintainable database code and processes.
  • Proactive problem solver who takes ownership of incidents and drives timely resolution and prevention strategies.
  • Team player who mentors junior colleagues and contributes positively to engineering practices and culture.
  • Time management and prioritization in fast-paced environments with multiple competing requests and SLAs.
  • Customer-centric approach to delivering data products, balancing speed and quality to meet business needs.
  • Adaptability to new technologies and continuous learning mindset — stays current with trends in cloud databases, analytics and automation.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Mathematics, Statistics, or a related technical field.

Preferred Education:

  • Master's degree in Data Science, Computer Science, Information Systems or MBA with analytics emphasis.
  • Professional certifications (recommended): Microsoft Certified: Azure Database Administrator Associate, Oracle Database SQL Certified Associate, Snowflake SnowPro, AWS Certified Database – Specialty, CDMP (Certified Data Management Professional).

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Information Technology
  • Data Science / Analytics
  • Mathematics / Statistics
  • Business Intelligence / Management Information Systems

Experience Requirements

Typical Experience Range: 2–6 years of hands-on experience working with relational databases, SQL development, ETL pipelines and reporting solutions.

Preferred: 4+ years in enterprise environments with demonstrable database performance tuning, data modeling, and cloud migration experience; experience supporting high-availability production systems and SLA-driven operations.

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