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Key Responsibilities and Required Skills for Data Operations Analyst

💰 $ - $

🎯 Role Definition

This role requires a pragmatic, detail-oriented Data Operations Analyst to ensure reliable, performant, and high-quality data delivery across our analytics and operational systems. The Data Operations Analyst will own monitoring and incident response for ETL and ELT pipelines, collaborate closely with data engineering and analytics teams to implement data quality checks and observability, and continuously improve automation and operational runbooks. This role requires strong SQL and scripting skills, experience with orchestration tools (Airflow, Prefect), familiarity with cloud data warehouses (Snowflake, BigQuery, Redshift), and a customer-focused approach to supporting internal stakeholders and dashboards.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning into operational ownership of pipelines and datasets
  • Junior Data Engineer or ETL Developer with hands-on pipeline experience
  • Business Intelligence Analyst who supports reports and dashboard reliability

Advancement To:

  • Senior Data Operations Analyst / Lead DataOps Engineer
  • Analytics Engineering Lead or Senior Data Engineer
  • Data Operations Manager or Head of Data Reliability

Lateral Moves:

  • Analytics Engineer (dbt / modeling-focused)
  • Business Intelligence Engineer or BI Manager

Core Responsibilities

Primary Functions

  • Monitor, triage, and resolve production incidents for ETL/ELT and data ingestion pipelines across cloud platforms, reducing mean time to resolution (MTTR) and ensuring SLA adherence for data availability.
  • Design, implement, and maintain automated data quality checks, validation rules, and anomaly detection to prevent incorrect or incomplete data from reaching analytics consumers.
  • Build, maintain, and optimize data pipelines using orchestration frameworks such as Apache Airflow, Prefect, or other schedulers to ensure timely, reliable data loads into data warehouses like Snowflake, BigQuery, or Redshift.
  • Develop and maintain SQL queries, Python scripts, and shell utilities to extract, transform, and load data; implement idempotent and performance-conscious ETL patterns to minimize downstream failures.
  • Investigate root causes of pipeline failures, implement permanent fixes, and produce post-incident reviews and remediation plans to eliminate recurring issues.
  • Implement pipeline observability and monitoring solutions (metrics, alerts, dashboards, and logs) that notify teams of SLA breaches, data drift, schema changes, and performance regressions.
  • Maintain data lineage and metadata documentation across the ingestion and transformation layers to improve traceability for analytics, reporting, and compliance use cases.
  • Collaborate closely with data engineers and analytics teams to design scalable transformations, promote best practices (modular SQL, testing, documentation), and support CI/CD for analytics code (dbt, git workflows).
  • Execute and coordinate scheduled deployments and migrations for data pipelines and transformation code, including rollback procedures and cross-team communication during releases.
  • Create and maintain runbooks, playbooks, and standard operating procedures (SOPs) for common operational tasks, incident response, onboarding, and handoffs between teams.
  • Partner with product managers, analysts, and business stakeholders to prioritize operational improvements and translate business requirements into robust, measurable data operations tasks.
  • Conduct regular data reliability audits and implement corrective actions to align datasets with business definitions, KPIs, and reporting SLAs.
  • Automate routine operational tasks, such as data backfills, retries, and maintenance windows, to reduce manual intervention and increase reproducibility of fixes.
  • Optimize query performance and transformation logic in the data warehouse, using partitioning, clustering, incremental models, and cost-aware design patterns to reduce compute and storage costs.
  • Support schema evolution and coordinate schema change management, including impact assessment, migration strategies, and communication with downstream consumers.
  • Enforce data governance standards for access control, data classification, and PII handling by collaborating with security, legal, and data governance teams.
  • Implement and maintain unit and integration tests for pipeline transformations using framework-appropriate testing (dbt tests, pytest, assertion checks) to detect regressions early.
  • Manage and maintain operational dashboards and internal status pages that reflect pipeline health, job runtimes, error rates, and SLA compliance for stakeholders.
  • Lead onboarding and knowledge transfer with cross-functional teams to ensure business users understand data availability windows, data definitions, and how to request data support.
  • Coordinate capacity planning, resource allocation, and cost monitoring in cloud environments to ensure operational continuity at scale while controlling spend.
  • Support regulatory and compliance reporting requirements by maintaining auditable logs, provenance records, and access histories for critical datasets.

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)

  • Strong SQL expertise for query development, optimization, and troubleshooting across large datasets and analytic workloads.
  • Proficient in a scripting language such as Python or Bash for automation, ETL helpers, and lightweight transformations.
  • Hands-on experience with orchestration tools (Apache Airflow, Prefect, Luigi) and designing DAGs with retry, SLA, and alerting logic.
  • Familiarity with modern data warehouse platforms: Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse.
  • Experience with ELT/ETL frameworks and analytics engineering tools such as dbt; knowledge of incremental models and testing practices.
  • Data quality and validation tooling experience (Great Expectations, Monte Carlo, custom assertion frameworks) and implementing monitoring at scale.
  • Working knowledge of cloud platforms (AWS, GCP, Azure) including IAM, storage (S3/GCS), and serverless/compute services used for data workloads.
  • Version control and CI/CD experience (git, GitHub/GitLab actions, Jenkins) for deployment of data transformation code and infrastructure as code.
  • Familiarity with BI and visualization tools (Looker, Tableau, Power BI) for supporting downstream dashboards and interpreting stakeholder issues.
  • Experience implementing logging, metrics, and observability (Prometheus, Grafana, Datadog, CloudWatch) for data pipelines and jobs.
  • Understanding of data modeling concepts and best practices for analytics (star schemas, slowly changing dimensions, normalization).
  • Knowledge of data governance, data privacy regulations (GDPR, CCPA basics), and secure access patterns for sensitive data.

Soft Skills

  • Strong stakeholder management and communication: able to translate technical incidents into business impact and explain mitigation plans to non-technical audiences.
  • Problem-solving mindset with a bias for root-cause analysis and durable fixes rather than temporary workarounds.
  • Prioritization and time management: able to balance incident response, engineering work, and stakeholder requests under SLAs.
  • Collaborative team player who partners effectively with data engineers, analysts, product managers, and security teams.
  • Attention to detail and high ownership: documentation-driven and committed to improving operational reliability.
  • Adaptability and continuous learning: comfortable working in rapidly evolving data stacks and adopting new tools or patterns.
  • Ability to write clear runbooks and post-mortem reports that facilitate organizational learning and operational maturity.
  • Customer-focused orientation: proactive in identifying pain points, reducing manual effort, and improving the data consumer experience.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or related field — OR equivalent practical experience.

Preferred Education:

  • Master’s degree in a related quantitative or technical discipline is beneficial but not required; relevant industry certifications (AWS/GCP/Azure, dbt, data engineering) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Mathematics / Statistics
  • Software Engineering

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on experience in data operations, ETL/ELT development, data engineering support, or analytics engineering.

Preferred:

  • 3+ years managing production data pipelines and observability; demonstrable experience with cloud data warehouses (Snowflake/BigQuery/Redshift), orchestration (Airflow), SQL optimization, and automated testing frameworks. Prior exposure to DataOps practices, data governance programs, or enterprise BI deployments is highly desirable.
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