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Key Responsibilities and Required Skills for Data Administrator

💰 $ - $

Data ManagementITAnalyticsDatabase Administration

🎯 Role Definition

As a Data Administrator, you are the operational owner of the organization's structured and semi-structured data stores. You ensure data is accurate, available, secure, and performant for analytics, reporting, and application consumption. This role blends database administration, ETL and pipeline operations, data quality stewardship, metadata management, and cross-functional stakeholder support. Ideal candidates are skilled in SQL, data modeling, data governance, system monitoring, and have practical experience with cloud data platforms and ETL/automation tools.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning into platform and data operations
  • Junior Database Administrator or DBA Assistant
  • IT Support / Systems Administrator with database responsibilities

Advancement To:

  • Senior Data Administrator / Lead Data Administrator
  • Data Architect or Enterprise Data Architect
  • Data Operations Manager / Head of Data Operations
  • Data Engineering Manager or Director of Data Services

Lateral Moves:

  • Data Engineer
  • Business Intelligence (BI) Developer
  • Data Governance Specialist

Core Responsibilities

Primary Functions

  • Manage and maintain production and development database environments (relational and NoSQL), including provisioning, configuration, patching, performance tuning, indexing strategies, and capacity planning to ensure optimal uptime and query performance.
  • Design, implement, and operate robust ETL/ELT pipelines using tools such as SSIS, Informatica, Talend, Airflow, or cloud-native services to reliably ingest, transform, and load data from multiple sources into data warehouses and data lakes.
  • Author and optimize SQL queries, stored procedures, views, and functions for ingestion, transformation, reporting, and troubleshooting complex data issues while ensuring execution plans and index usage are efficient.
  • Define, implement, and enforce data quality checks, validation rules, reconciliation processes, and automated alerts to detect and remediate duplicates, inconsistencies, stale records, and schema drift in production datasets.
  • Maintain and govern metadata and data catalog entries, create and update data dictionaries, lineage documentation, and business glossaries so analysts and engineers can discover, understand, and use datasets correctly.
  • Manage access control, user roles, privileges, and authentication mechanisms (LDAP, AD, IAM) for databases and analytics platforms; perform periodic access reviews to meet least-privilege and compliance requirements.
  • Implement backup, disaster recovery, and high-availability strategies including backup scheduling, point-in-time recovery testing, replication, and multi-region failover where applicable.
  • Monitor health and performance of data infrastructure with observability tooling (Prometheus, Grafana, CloudWatch, SQL Server Monitoring); triage and remediate incidents and outages with on-call responsibilities and runbooks.
  • Collaborate with data engineers, data scientists, analysts, and product teams to translate business requirements into schema designs, data models (star/snowflake), and optimized table structures that support reporting and analytics SLAs.
  • Lead and execute data migrations, upgrades, and platform consolidation projects with minimal downtime by developing cutover plans, rollback procedures, and data verification strategies.
  • Establish and maintain CI/CD pipelines for database code, migration scripts, and ETL deployments using Git, Jenkins, Azure DevOps, or GitHub Actions to promote repeatable and auditable rollouts.
  • Enforce and operationalize data governance policies including PII/PHI handling, retention schedules, masking/anonymization techniques, and regulatory compliance (GDPR, CCPA, HIPAA) in partnership with legal and compliance teams.
  • Create, maintain, and deliver technical documentation, runbooks, standard operating procedures (SOPs), and knowledge base articles for common tasks and troubleshooting flows.
  • Design and manage data retention, archiving, and purging policies to control storage costs and maintain query performance while complying with legal and business requirements.
  • Implement schema change management and migration processes that minimize breaking changes for downstream consumers and ensure backward compatibility where necessary.
  • Perform root cause analysis for recurring data incidents, produce postmortems with actionable remediation steps, and drive continuous improvement initiatives to reduce mean time to recovery (MTTR).
  • Integrate on-premise and cloud data sources, manage connectors and ingestion frameworks, and troubleshoot connectivity, authentication, and format/serialization issues (CSV, JSON, Avro, Parquet).
  • Automate repetitive data administration tasks with scripts and orchestration (Python, Bash, PowerShell) to increase operational efficiency and reproducibility.
  • Support BI and analytics teams by provisioning datasets, creating optimized extracts, materialized views, and ensuring SLA-driven data refresh cycles for dashboards and reports.
  • Implement and tune partitioning, clustering, compression, and columnar storage strategies in data warehouses (Snowflake, BigQuery, Redshift) to reduce query costs and improve performance.
  • Collaborate on cost optimization initiatives for cloud data platforms by rightsizing instances, optimizing storage tiers, and advising on data lifecycle policies.
  • Conduct periodic data audits, reconcile source-to-target data, and prepare evidence for internal and external audits to assure data integrity and compliance.

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.
  • Train and mentor junior data admins, DBAs, and analysts on best practices for data ingestion, querying, and governance.
  • Work with vendors and managed service providers to evaluate third-party data tools and negotiate SLAs.
  • Participate in forecasting and budgeting for data infrastructure, licenses, and cloud spend.
  • Assist in prototyping proof-of-concept solutions for new analytics capabilities or data platform enhancements.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL expertise: query optimization, indexing, execution plans, window functions, CTEs.
  • Hands-on experience with relational databases: Microsoft SQL Server, Oracle, MySQL, PostgreSQL.
  • Familiarity with cloud data platforms and warehouses: AWS (Redshift, RDS), Azure (Synapse, SQLDB), GCP (BigQuery), Snowflake.
  • ETL/ELT tools and orchestration: SSIS, Informatica, Talend, Apache Airflow, dbt, AWS Glue.
  • Scripting and automation: Python, Bash, PowerShell for task automation and data validation.
  • Data modeling and schema design: star schema, snowflake, normalization, and denormalization strategies.
  • Data quality and profiling tools/techniques: anomaly detection, reconciliation, sampling, rule-based validation.
  • Metadata management and data catalog experience: Collibra, Alation, Apache Atlas or homegrown catalogs.
  • Backup, recovery, HA, and replication strategies for databases; experience with snapshots and point-in-time recovery.
  • Monitoring and observability tools: Prometheus, Grafana, New Relic, Datadog, CloudWatch, SQL Server Monitoring solutions.
  • Security and compliance: IAM, role-based access control, encryption at-rest/in-transit, PII masking and anonymization.
  • Version control and CI/CD for database artifacts: Git, Liquibase, Flyway, Azure DevOps, Jenkins.
  • Familiarity with BI/reporting tools ingestion needs: Power BI, Tableau, Looker, Qlik and best practices for serving analytical workloads.
  • Knowledge of data formats and storage technologies: Parquet, Avro, ORC, JSON, CSV, columnar storage concepts.
  • Experience with NoSQL and streaming systems (optional but preferred): MongoDB, Cassandra, Kafka, Kinesis.

Soft Skills

  • Strong analytical and troubleshooting skills with meticulous attention to data accuracy and detail.
  • Excellent communication and stakeholder management; able to translate technical constraints into business impact.
  • Proactive problem-solver who documents decisions and drives follow-through on action items.
  • Time management and prioritization skills in a fast-paced environment with competing SLAs.
  • Collaborative team player who contributes to culture, mentoring, and cross-functional initiatives.
  • Adaptability to new tools, cloud platforms, and shifting data governance requirements.
  • Customer-service orientation for internal BI, analytics, and product teams.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree (MS) in Information Systems, Data Science, Computer Science, or MBA with analytics focus highly desirable.
  • Professional certifications (optional): Microsoft Certified: Azure Database Administrator, AWS Certified Database - Specialty, Snowflake Advanced Architect, Google Cloud Professional Data Engineer.

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Information Technology
  • Data Science / Applied Statistics
  • Software Engineering
  • Business Analytics

Experience Requirements

Typical Experience Range:

  • 2–7 years of progressive experience in database administration, data operations, or data engineering; entry-level roles may accept 1–2 years with strong technical aptitude.

Preferred:

  • 3+ years managing production data systems and ETL pipelines in a mid-size to large enterprise.
  • Demonstrated experience with cloud data platforms and implementing data governance or data quality programs.
  • Prior exposure to compliance and audit processes (GDPR, CCPA, HIPAA) preferred.
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