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Key Responsibilities and Required Skills for Data Operations Lead

💰 $ - $

Data OperationsData EngineeringAnalyticsData PlatformCloud

🎯 Role Definition

The Data Operations Lead owns the day-to-day operational stability and continuous improvement of the organization's data platform and production data pipelines. This role drives availability, performance, cost efficiency, and compliance for ETL/ELT workflows, data warehouses, streaming systems, and supporting infrastructure. The Data Operations Lead partners with Data Engineering, Analytics, Security, and Product teams to translate business SLAs into operational processes, automation, and metrics.

Key outcomes include: 99x% pipeline reliability, reduced mean-time-to-recover (MTTR) for data incidents, predictable deployment lifecycle for data infra changes, and measurable improvements in data quality, latency, and cost.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with production support responsibilities
  • Data Platform Engineer or ETL/ELT Lead
  • Analytics Engineering Lead or Site Reliability Engineer (SRE) for data

Advancement To:

  • Head of Data Operations / Director of Data Platform Operations
  • Director of Data Engineering or VP of Data Platforms
  • Chief Data Officer (with broader strategy + governance remit)

Lateral Moves:

  • Data Engineering Manager
  • Analytics Engineering Manager
  • Data Product Manager

Core Responsibilities

Primary Functions

  • Lead the operational lifecycle for all production data pipelines and data platform components (batch and streaming), establishing clear ownership, runbooks, escalation paths, and measurable SLAs to ensure consistent data availability for business users and downstream systems.
  • Architect, implement, and continuously improve monitoring, alerting, and observability for data workflows, including defining meaningful metrics (latency, throughput, error rates, ingestion freshness) and creating dashboards and SLOs that drive actionability and accountability.
  • Own incident management for data-related outages: lead on-call rotations, coordinate triage and postmortems, identify root causes, drive remediation and preventive actions, and publish communication to stakeholders and leadership.
  • Design and operate reliable ETL/ELT orchestration using tools such as Apache Airflow, dbt, Prefect, or managed orchestrators; ensure DAG best practices, dependency management, and idempotent job design are enforced across teams.
  • Implement and enforce data quality frameworks and automated testing for pipelines, integrating unit, integration, regression, and schema-change tests into CI/CD pipelines to prevent regressions and data corruption in production.
  • Manage cloud data platform operations (Snowflake, BigQuery, Redshift, Databricks, Azure Synapse), optimizing compute patterns, storage, partitioning, and cost; perform resource sizing, query tuning, and job concurrency planning to meet performance targets.
  • Build, maintain, and govern metadata, data cataloging, and lineage solutions (e.g., Amundsen, DataHub, Collibra) to accelerate troubleshooting, improve discoverability, and support compliance and audit requirements.
  • Define and operate capacity planning, performance testing, and release processes for data infrastructure changes; coordinate schema migrations, backfills, and data reprocessing with minimal customer impact.
  • Drive automation of repetitive operational tasks (scripted rollouts, healing scripts, automated restarts, backfill orchestration, dependency checks) to reduce manual toil and operational costs.
  • Develop and enforce security and compliance controls for production data operations, including access management, encryption at rest/in transit, PII handling, data retention policies, and audit logging in partnership with Security and Legal.
  • Lead cross-functional coordination for data platform projects, acting as the central operational liaison between product owners, engineering teams, analytics consumers, and SRE/DevOps to guarantee smooth launches and SLA alignment.
  • Create and maintain robust runbooks, playbooks, and documentation for common failure modes, escalations, and recovery procedures; ensure runbooks are kept current and accessible to the on-call team.
  • Own vendor and third-party integrations for data ingestion or transformation, managing contracts, operational SLAs, troubleshooting, and ensuring vendor solutions meet internal reliability and security standards.
  • Coach and mentor engineers on operational excellence, incident response discipline, observability best practices, and cloud cost consciousness; lead hiring and team development initiatives for data ops hires.
  • Establish and report on operational KPIs (MTTR, MTBF, pipeline success rate, data freshness SLAs, cost per TB processed) and run monthly/quarterly reviews with leadership to prioritize platform investments.
  • Lead change control and release management for production data infra, implementing feature flags, canary deployments, and rollback strategies to reduce deployment risk and accelerate safe delivery.
  • Implement data retention, archiving, and deletion workflows aligned with business and regulatory requirements; maintain compliance-ready records of data movement and retention actions.
  • Champion continuous improvement initiatives: post-incident remediation tracking, root-cause elimination projects, technical debt repayment, and runbook automation programs to increase platform maturity.
  • Support analytics and data science teams with productionizing models, feature pipelines, and data products, ensuring operational metrics, drift detection, and retraining processes are in place.
  • Collaborate with finance and procurement to forecast operating costs for the data platform, identify cost-saving opportunities (query optimization, tiering storage), and implement chargeback or showback models across business units.
  • Drive the adoption of DevOps and CI/CD practices for data engineering: automated testing, linting, schema validations, version control, and reproducible deployment artifacts to improve release reliability.
  • Ensure high-quality stakeholder communication by preparing incident summaries, compliance reports, postmortems, capacity forecasts, and regular status updates for technical and non-technical audiences.
  • Evaluate and pilot new platform technologies (streaming, orchestration, cataloging, observability) and create migration plans with minimal disruption to production workloads.
  • Enforce standardization across teams on schema conventions, data contract practices, API contracts, versioning strategies, and backward compatibility rules to reduce downstream breakages.
  • Plan and execute major reprocessing/backfill efforts safely (checkpointing, staging, sampling, validation), including automated validation steps to verify data integrity after large runs.

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 debugging, optimization, complex joins, window functions, and performance tuning on large datasets.
  • Hands-on experience with Python (or Scala/Java) for building operational tooling, automation, ETL/ELT jobs, and data validation scripts.
  • Proven operational experience with orchestration tools like Apache Airflow, dbt Cloud, Prefect or similar schedulers for workflow reliability and dependency management.
  • Deep familiarity with cloud data warehouses and analytics platforms: Snowflake, BigQuery, Redshift, Databricks, or Azure Synapse; ability to optimize compute/storage and query performance.
  • Strong knowledge of streaming technologies (Kafka, Kinesis, Pub/Sub) and exactly-once/at-least-once delivery patterns, offset management, and monitoring for real-time pipelines.
  • Experience implementing data observability and quality tools (Great Expectations, Monte Carlo, Soda, Deequ) and integrating quality checks into CI/CD.
  • Proficiency with monitoring and alerting stacks (Prometheus, Grafana, Datadog, New Relic) and designing actionable alerts to minimize noise and reduce MTTR.
  • Practical experience with infrastructure as code and CI/CD for data (Terraform, CloudFormation, GitHub Actions, Jenkins) and deployment strategies for data infra.
  • Familiar with data cataloging, lineage, and governance tools (Amundsen, DataHub, Collibra) and metadata management best practices.
  • Knowledge of security, compliance, and data privacy requirements (GDPR, CCPA), including RBAC, encryption, data masking, and audit logging.
  • Experience with cost optimization strategies for cloud data platforms, including resource scheduling, query cost analysis, and storage lifecycle policies.
  • Ability to design and implement schema migration strategies, versioning, and compatibility testing to prevent production breakage.

Soft Skills

  • Strong operational leadership and incident-command presence under pressure with demonstrated ability to lead cross-functional incident response and stakeholder communication.
  • Excellent written and verbal communication skills for crafting runbooks, postmortems, status reports, and executive updates.
  • Proven stakeholder management—able to translate technical tradeoffs into business impact and persuade non-technical partners on prioritization.
  • Mentorship and people management skills, able to grow and coach engineers in reliability practices and career progression.
  • Analytical problem solving and systems thinking—able to decompose failure modes, quantify impact, and propose pragmatic remediation paths.
  • High attention to detail with a bias for automation and repeatability to reduce manual intervention and operational toil.
  • Project and time-management skills to coordinate complex reprocessing efforts, releases, and multi-team initiatives.
  • Customer-obsessed mindset with the ability to prioritize reliability and data quality for internal analytics consumers and external customers.
  • Change management and process design experience to institutionalize best practices and drive cultural shifts toward operational excellence.
  • Adaptability and continuous learning orientation to evaluate new technologies and incorporate them appropriately into the platform.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Information Systems, Business Analytics, or related field is a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 5–10+ years in data engineering, data platform operations, site reliability engineering for data systems, or related roles.

Preferred:

  • 7+ years with demonstrable experience building and operating large-scale data platforms in the cloud, leading incident response, implementing observability and data quality frameworks, and managing cross-functional teams.

If you would like, I can tailor this description for a specific industry (e.g., fintech, healthcare, adtech), company size (startup vs. enterprise), or include a sample interview question set and scorecard for hiring.

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