Key Responsibilities and Required Skills for Data Operations Specialist

🎯 Role Definition

The Data Operations Specialist (also referred to as DataOps Specialist, Data Reliability Engineer, or Data Operations Engineer) is responsible for ensuring reliable, scalable, and well-governed data pipelines and platforms that power analytics, BI, and ML. This role blends operations, engineering, and stakeholder coordination to monitor and maintain ETL/ELT processes, enforce data quality, manage incidents, optimize performance, and automate repetitive tasks. The ideal candidate has strong SQL and scripting skills, experience with orchestration tools (e.g., Apache Airflow), cloud data warehouses (e.g., Snowflake, BigQuery, Redshift), and a proven track record of improving data reliability and observability.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with strong SQL and pipeline experience
Site Reliability Engineer (SRE) or Platform Engineer transitioning to data platforms
ETL Developer or BI Engineer expanding into operations and automation

Advancement To:

Senior Data Operations / Data Reliability Engineer
Data Platform Lead / Manager
Head of Data Engineering or Director of Data Operations

Lateral Moves:

Data Engineer (specializing in pipelines)
ML Infrastructure Engineer
Data Governance or Data Quality Lead

Core Responsibilities

Primary Functions

Monitor, manage, and remediate failures in production data pipelines and ETL/ELT jobs across batch and streaming workflows, ensuring SLA compliance and minimizing data downtime.
Design, implement, and maintain observability and alerting for data workflows (task-level and dataset-level), including metrics, logs, traces, and runbook-driven incident response.
Author and automate robust runbooks and incident playbooks for recurring failure modes, documenting root-cause analyses and corrective action plans to reduce recurrence.
Build and maintain orchestration and scheduling solutions (e.g., Apache Airflow, Prefect, Dagster), configuring DAGs, task retries, SLAs, backfills, and dependency graphs for resilient execution.
Own onboarding, configuration, and operational health of cloud data platforms (Snowflake, BigQuery, Redshift) and their integration with ingestion frameworks and transformation tools.
Implement and own data quality frameworks and automated tests (unit, regression, schema checks, anomaly detection) to validate datasets before promotion to production and downstream consumers.
Develop and maintain automated CI/CD pipelines for data infrastructure code, SQL, transformation repositories (dbt), and deployment of data platform artifacts using GitOps best practices.
Manage data ingestion and integration from diverse sources (databases, streaming systems like Kafka, APIs, third-party SaaS) including schema evolution handling and connector operations.
Perform proactive capacity planning, cost monitoring, and query optimization to improve warehouse performance and control cloud spend while meeting SLAs.
Implement access control, encryption, and data masking strategies in collaboration with security and governance teams to enforce least privilege and compliance requirements (GDPR, CCPA, HIPAA where applicable).
Coordinate cross-functional incident response between data engineering, platform, DevOps, and business stakeholders, communicating impact, timelines, and mitigation status clearly.
Create and maintain lineage and metadata capture processes using data catalog tools or custom solutions to enable traceability from source to report and support data governance.
Triage and prioritize incoming data incidents and support requests, providing timely root cause analysis and permanent fixes rather than one-off mitigations.
Automate repetitive operational tasks (retries, backfills, schema migrations) via scripts, tooling, or small services to reduce manual toil and mean-time-to-repair (MTTR).
Evaluate, onboard, and integrate third-party tools for observability, data quality, and metadata management (e.g., Monte Carlo, Great Expectations, Collibra, Atlan) and measure their ROI.
Maintain and improve data contract enforcement between producers and consumers, including schema compatibility checks, SLAs, and backward/forward compatibility policies.
Partner with data engineers and analysts to produce and maintain data artifact documentation, deployment guides, and runbooks to enable team scalability and knowledge transfer.
Conduct regular post-incident reviews and retrospectives, converting lessons learned into improved tooling, processes, and training for the broader data organization.
Implement monitoring for data drift, missing partitions, late-arriving data, and unexpected distribution changes; surface these to product owners and trigger automated remediation where possible.
Ensure high availability and disaster recovery practices for critical data assets and pipelines, including backup verification, failover procedures, and DR runbooks.

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.
Mentor junior data operations or data engineering staff on operational best practices, observability, and incident handling.
Run periodic audits of pipeline health, data freshness, and schema compatibility to present to leadership.
Drive continuous improvement initiatives to reduce cost, increase throughput, and improve pipeline reliability.
Liaise with security, compliance, and privacy teams to operationalize data governance policies across pipelines.
Develop alert-runbook triage matrices to reduce alert fatigue and increase signal-to-noise in monitoring.
Maintain a backlog of technical debt items and collaborate with engineering to schedule remediation work.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL for troubleshooting, dataset validation, partitioning strategies, and query tuning across large-scale data warehouses.
Proficiency in at least one scripting language (Python, Bash, or similar) for automation, tooling, and small services.
Experience with data orchestration tools such as Apache Airflow, Prefect, Dagster, or similar; comfortable authoring DAGs and operationalizing workflows.
Hands-on experience with cloud data warehouses and analytics platforms: Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse.
Familiarity with ELT/ETL frameworks and transformation tooling (dbt, Matillion, Fivetran, Stitch) and principles of modular, testable transformations.
Knowledge of streaming platforms and integrations (Kafka, Kinesis, Pub/Sub) including monitoring and ingestion patterns for real-time pipelines.
Proven ability to design and implement data quality and validation frameworks (Great Expectations, Deequ, custom checks).
Experience implementing observability and monitoring stacks (Prometheus, Grafana, Datadog, Cloud Monitoring) for data workloads.
Competence with CI/CD tooling and Git-based workflows for data artifacts (GitLab CI, GitHub Actions, Jenkins).
Understanding of data modeling, schema evolution, partitioning, and performance implications for analytical workloads.
Familiarity with metadata management and data catalog tools (Atlan, Amundsen, DataHub) and implementing lineage capture.
Strong knowledge of security best practices for data platforms: IAM, role-based access control, encryption (in transit and at rest), and masking techniques.
Experience with cloud platforms (AWS, GCP, Azure) including managed services, cost optimization, and networking basics.
Ability to instrument pipelines to capture metrics, set SLAs, and build dashboards to measure freshness, throughput, and success rate.
Comfortable using ticketing and incident management systems (JIRA, PagerDuty, Opsgenie) and running postmortems.

Soft Skills

Strong communication and stakeholder management: explain technical incidents and impacts to non-technical audiences and executives.
Problem solving and root-cause analysis: structured approach to diagnosing complex production issues under pressure.
Prioritization and time management: balance incident response, technical debt, and long-term reliability projects.
Collaboration and teamwork: work cross-functionally with data engineers, platform, security, and analytics teams.
Detail-oriented and process-driven: enforce repeatable processes to reduce repeat incidents and improve quality.
Continuous learning mindset: adapt to evolving data stack technologies and suggest pragmatic improvements.
Customer-focused orientation: treat internal teams as customers and deliver timely, well-documented solutions.
Coaching and mentorship: guide junior team members on operational best practices and troubleshooting techniques.
Resilience and composure during high-severity incidents, with the ability to lead or support war-rooms calmly.
Strategic thinking: contribute to the design of scalable, maintainable, and cost-effective data operations practices.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or related quantitative discipline.
Certifications in cloud platforms (AWS/GCP/Azure), data engineering, or data governance tools are a plus.

Relevant Fields of Study:

Computer Science
Data Engineering / Information Systems
Software Engineering
Mathematics, Statistics, or Applied Data Science
Information Security / Cybersecurity (beneficial for governance-heavy roles)

Experience Requirements

Typical Experience Range:

2–6+ years in data engineering, data operations, SRE, or similar roles that include production pipeline and platform ownership.

Preferred:

4+ years managing production data pipelines and data platform operations in cloud environments.
Demonstrated experience reducing MTTR, implementing data observability, and operationalizing data quality frameworks in a cross-functional environment.
Prior experience with one or more major cloud data warehouses, orchestration tools, and implementing CI/CD for data artifacts.