Torchora
Back to Home

Key Responsibilities and Required Skills for Data Operations Manager

💰 $ - $

DataOperationsManagementAnalyticsEngineering

🎯 Role Definition

The Data Operations Manager owns the day-to-day reliability, observability, and continuous improvement of an organization's data products, pipelines, and platform. This role leads operational processes for ETL/ELT, data quality, cataloging, incident response, SLA management, and cross-functional stakeholder enablement. The Data Operations Manager drives automation to reduce toil, governs data access and compliance, and partners with engineering, analytics, and business teams to ensure secure, accurate, and timely delivery of data for decision-making and analytics.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with a focus on production reliability and workflow orchestration
  • Data Engineering Lead or Data Platform Engineer transitioning into operations leadership
  • Analytics Engineering or BI Manager with experience in productionizing data pipelines

Advancement To:

  • Head of Data Operations / Director of Data Engineering Operations
  • Director of Data Platforms or Chief Data Officer (CDO) in mid-sized organizations

Lateral Moves:

  • Data Platform Product Manager
  • Data Governance Lead
  • Analytics Engineering Manager

Core Responsibilities

Primary Functions

  • Own and run the operational lifecycle for ETL/ELT and streaming data pipelines, ensuring 99%+ data availability and timeliness by implementing robust monitoring, automated recovery, and runbook-driven incident processes.
  • Design, implement, and maintain data observability and monitoring stacks (metrics, alerts, dashboards, lineage) to proactively detect anomalies, regressions, and data quality issues across Snowflake, BigQuery, Redshift, or equivalent data warehouses.
  • Lead incident management for data incidents and pipeline failures: triage, root-cause analysis (RCA), post-mortems, corrective actions, and continuous improvement to prevent recurrence and reduce mean time to recovery (MTTR).
  • Define and enforce data SLAs and operational KPIs for data freshness, completeness, accuracy, and latency in collaboration with analytics, product, and business stakeholders.
  • Implement and steward data quality frameworks, including automated tests, assertions, and checks (schema, null rates, distribution drift) using tools like Great Expectations, dbt tests, or custom validation logic.
  • Build and maintain orchestration and scheduling frameworks (Airflow, Dagster, Prefect) and author reliable DAGs/workflows with idempotency, retries, and failure handling for large-scale pipeline execution.
  • Lead change control, deployment, and release management processes for data pipelines and platform components to minimize risk and coordinate cross-functional releases.
  • Automate repetitive operational tasks—reprocessing, backfill orchestration, environment provisioning—to reduce manual toil and scale the operations function using infrastructure-as-code and CI/CD for data.
  • Manage and optimize data infrastructure costs across cloud providers (AWS, GCP, Azure) by right-sizing compute, optimizing storage formats (Parquet/ORC), partitioning strategies, and query performance tuning.
  • Build and maintain a data catalog, metadata management, and lineage system to improve discoverability, compliance, and impact analysis for data consumers and downstream systems.
  • Define, document, and socialize runbooks, operational playbooks, SLAs, and escalation paths for both technical and non-technical stakeholders to improve incident response and transparency.
  • Partner with security, privacy, and legal teams to operationalize data access controls, role-based access, masking, encryption, and compliance for GDPR, CCPA, HIPAA, and other regulatory requirements.
  • Drive capacity planning and performance tuning for data processing clusters, warehouses, and streaming platforms to meet peak load requirements and predictable SLAs.
  • Mentor and manage a team of Site Reliability Engineers (SREs), DataOps engineers, and platform engineers focused on data reliability, automation, and observability practices.
  • Collaborate with data engineering and analytics engineering teams to define production readiness criteria, runbook requirements, and data contract management for internal data product teams.
  • Maintain vendor relationships and evaluate third-party data ops tooling (observability, catalog, lineage, ETL) to augment internal capabilities and accelerate operations maturity.
  • Establish and run operational review cadences—weekly runbooks reviews, monthly SLA reviews, and quarterly platform retrospectives—to measure progress and prioritize operational debt.
  • Implement and operationalize testing strategies for data pipelines: unit testing, integration testing, regression testing, and synthetic data replay to validate end-to-end behavior before production release.
  • Lead cross-functional root cause investigations that involve networks, cloud infra, and application teams to solve complex systemic issues impacting data delivery and accuracy.
  • Create and maintain dashboards that report data health, pipeline throughput, job success rates, cost per workload, and user-facing SLA metrics for executives and data consumers.
  • Drive cultural change toward proactive data reliability, observability, and engineering excellence by evangelizing best practices for schema evolution, contract testing, and iterative delivery.
  • Coordinate disaster recovery and business continuity planning for critical data pipelines and platform components, including documented RTO/RPO targets and validated failover procedures.
  • Ensure robust data provenance and auditability for financial, legal, or compliance-sensitive datasets through immutable logs, access trails, and versioned datasets.
  • Lead data migration and large-scale backfill projects with minimal consumer impact by designing phased rollouts, parallel runs, and consumer communication plans.

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 skills for complex query optimization, debugging and validating dataset correctness across large data warehouses.
  • Hands-on experience with cloud data platforms (Snowflake, BigQuery, Amazon Redshift, Azure Synapse) and managing operational aspects of these systems.
  • Proficiency with workflow orchestration tools such as Apache Airflow, Dagster, Prefect, or similar for scheduling, retry logic, and DAG management.
  • Practical experience with data transformation frameworks and tooling (dbt, Spark, flink) and implementing production-ready ETL/ELT patterns.
  • Data observability and quality tooling experience: Great Expectations, Monte Carlo, Databand, Soda, or building custom assertion frameworks.
  • Strong knowledge of data modeling, partitioning, and storage formats (Parquet, ORC, Avro) to optimize performance and cost.
  • Experience with programming languages used in data ops: Python (preferred), Java/Scala, or SQL-based procedural code for automation and orchestration scripts.
  • Familiarity with monitoring, logging, and APM tooling (Prometheus, Grafana, Datadog, New Relic, ELK) for pipeline health and metrics.
  • Knowledge of cloud infrastructure and IaC tools (Terraform, CloudFormation) for reproducible environment provisioning and platform changes.
  • Hands-on experience implementing data access controls, encryption, and tokenization for compliance (GDPR, HIPAA, CCPA), and knowledge of IAM best practices.
  • Proven ability to run RCA, create post-mortems, track action items, and follow through on operational improvements.
  • Experience with CI/CD pipelines for data code and infrastructure changes (GitHub Actions, Jenkins, GitLab CI) to enable safe rollouts.
  • Familiarity with streaming and event-driven platforms (Kafka, Pub/Sub, Kinesis) and operational patterns for real-time data delivery.
  • Ability to build and maintain data catalogs and metadata management solutions (Collibra, Alation, Amundsen, DataHub).
  • Cost optimization and capacity planning experience across cloud data services.

Soft Skills

  • Strong stakeholder management and cross-functional communication: translate technical issues into business impact and manage expectations.
  • Leadership and people management: hire, mentor, and grow a team of data operations engineers and platform SREs.
  • Problem-solving mindset with a focus on root-cause analysis, pragmatic trade-offs, and continuous improvement.
  • Project management and prioritization skills: balance operational firefighting with long-term roadmap initiatives and technical debt reduction.
  • Service-oriented mindset: customer-focused approach to enable internal data consumers and product teams.
  • High attention to detail while maintaining the ability to synthesize and communicate at an executive level.
  • Resilience and calm under pressure during high-severity incidents and downtime.
  • Proactive documentation and knowledge-sharing habits: maintain runbooks, onboarding docs, and operational playbooks.
  • Change management and influence skills to drive adoption of new tooling, processes, and automation.
  • Analytical mindset with the ability to define and measure meaningful operational KPIs and outcomes.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Computer Science, Business Analytics, or MBA with technology focus.
  • Certifications in cloud platforms (AWS/GCP/Azure), data governance (CDMP), or SRE/DataOps methodologies are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 5–10+ years in data engineering, data platform, or site reliability roles with at least 2–4 years leading operational teams or owning production data reliability.

Preferred:

  • Prior experience as a Data Operations Manager, Data Platform Lead, SRE for data services, or Senior Data Engineer with strong production ops ownership.
  • Demonstrated track record of improving data SLAs, implementing observability, reducing pipeline MTTR, and delivering cost-effective platform improvements.
  • Experience in regulated industries (healthcare, finance, advertising) with operational requirements for privacy, auditing, and compliance.
Explore More Careers