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Key Responsibilities and Required Skills for Data Operations Engineer

💰 $ - $

EngineeringDataOperationsData EngineeringDevOps

🎯 Role Definition

The Data Operations Engineer (Data Ops Engineer) is responsible for building and operating reliable, scalable, and observable data infrastructure that powers analytics, machine learning, and product features. This role combines elements of data engineering, site reliability engineering (SRE), and DevOps: owning ETL/ELT pipelines, ensuring data quality and lineage, implementing monitoring and alerting for data systems, and enabling self-service data workflows. A successful candidate drives improvements in data reliability, throughput, and cost-efficiency while collaborating closely with data scientists, analytics engineers, product managers, and platform teams.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to a platform/ops focus after building SQL and pipeline experience.
  • Junior Data Engineer or ETL Developer with exposure to production data workflows.
  • Site Reliability Engineer (SRE) or DevOps Engineer moving into specialized data infrastructure.

Advancement To:

  • Senior Data Operations Engineer / Staff Data Ops Engineer
  • Data Engineering Manager / Lead Data Platform Engineer
  • Head of Data Platform or Director of Data Engineering
  • Principal/Staff Engineer focusing on data platform reliability and architecture

Lateral Moves:

  • Data Engineer (focused on building new pipelines/features)
  • Analytics Engineer (dbt, BI-focused engineering)
  • DevOps / Platform Engineer (broader infrastructure responsibilities)

Core Responsibilities

Primary Functions

  • Design, build, and maintain robust, repeatable ETL/ELT data pipelines using tools such as Airflow, dbt, Spark, or cloud-native pipeline services; ensure pipelines meet SLA requirements for latency, throughput, and cost.
  • Implement system-level observability for data flows including metrics, structured logging, distributed tracing, and automated alerts to detect failed jobs, data drift, or schema changes.
  • Own deployment and lifecycle management of data infrastructure components (orchestration, streaming, warehouses, catalogs) in production on cloud platforms (AWS, GCP, Azure), using IaC tools like Terraform or CloudFormation.
  • Troubleshoot production incidents affecting data availability, quality, or timeliness; perform root cause analysis, document remediation steps, and implement preventive controls.
  • Define and enforce data quality frameworks and validation checks (e.g., unit tests, schema validation, Great Expectations) to detect anomalies and ensure accurate downstream analytics and ML.
  • Work with data cataloging and governance tools to maintain data lineage, schema evolution tracking, and metadata management to support compliance and discoverability.
  • Optimize query performance and storage costs in data warehouses (Snowflake, BigQuery, Redshift) through partitioning, clustering, compaction, and cost-aware design patterns.
  • Build and maintain streaming data platforms using Kafka, Kinesis, or Pub/Sub; design fault-tolerant, backpressure-aware consumers and processing topologies.
  • Create CI/CD pipelines for data code (SQL, dbt, Python, Spark jobs) including automated testing, linting, and deployment pipelines to production environments.
  • Collaborate with data scientists and ML engineers to productionize models, manage feature stores, and integrate model inference into data pipelines while ensuring reproducibility and version control.
  • Implement access controls, encryption, and data masking strategies in production to meet security, privacy, and regulatory requirements.
  • Conduct capacity planning and performance tuning for ETL clusters and data processing jobs to ensure predictable SLAs and cost-efficient resource utilization.
  • Develop tooling and internal developer experience (DX) to enable engineering teams to ship reliable data workflows faster (templates, libraries, SDKs).
  • Create runbooks, playbooks, and on-call rotations for data platform incidents and ensure smooth handovers across shifts and teams.
  • Coordinate cross-functional incident response and communicate timely status, mitigation plans, and postmortem findings to technical stakeholders and leadership.
  • Drive automation to eliminate manual intervention in data workflows (retries, checkpointing, schema migrations), reducing toil and improving MTTR (mean time to recovery).
  • Maintain comprehensive documentation for pipelines, data schemas, runbooks, and operational runbooks for new hires and cross-team collaboration.
  • Conduct regular data reliability reviews, SLAs, and capacity reviews with product and analytics stakeholders; translate business requirements into measurable SLOs/SLAs.
  • Lead or participate in migration projects (on-prem to cloud, warehouse changes, orchestration upgrades) ensuring data integrity, low downtime, and rollback strategies.
  • Implement blue/green or canary deployment patterns for critical data services and coordinate transparent release windows with downstream consumers.
  • Partner with analytics and product teams to onboard new data sources, validate ingestion patterns, and map data ownership and responsibilities across the organization.
  • Perform proactive corruption checks, schema drift detection, and retention policy enforcement to prevent accumulation of stale or corrupted datasets.
  • Evaluate and recommend new data infrastructure technologies and vendor solutions based on trade-offs in scalability, cost, and operational complexity.
  • Mentor junior engineers, run knowledge-sharing sessions on data operations best practices, and improve team processes for incident response and change management.

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.
  • Develop internal metrics dashboards (Grafana, Looker, Metabase) to communicate pipeline health, data freshness, and SLA adherence.
  • Assist with vendor integrations and evaluate managed services to balance operational overhead against customization needs.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL for complex transformations, performance tuning, and debugging of production queries.
  • Proficiency in Python (or Scala/Java) for ETL, orchestration scripts, testing harnesses, and automation.
  • Hands-on experience with workflow orchestration tools such as Apache Airflow, Prefect, or Dagster.
  • Experience with data transformation frameworks, especially dbt (data build tool) or equivalent ELT tooling.
  • Familiarity with cloud data warehouses and analytics platforms: Snowflake, Google BigQuery, Amazon Redshift.
  • Big data processing expertise: Apache Spark, Beam, Flink, or equivalent distributed compute frameworks.
  • Streaming data platform experience: Kafka, Kinesis, Pub/Sub, or Confluent ecosystem.
  • Infrastructure-as-Code and cloud provisioning: Terraform, CloudFormation, or Pulumi.
  • CI/CD & DevOps tooling for data: GitHub Actions, Jenkins, GitLab CI, or equivalent, and automated testing frameworks.
  • Monitoring and observability: Prometheus, Grafana, Datadog, Sentry, or Cloud-native monitoring tools.
  • Data quality and validation tools: Great Expectations, Monte Carlo, or in-house testing frameworks.
  • Security and governance basics: IAM, RBAC, encryption at rest/in transit, masking/tokenization, GDPR/CCPA considerations.
  • Containerization and orchestration: Docker, Kubernetes for scalable data processing workloads.
  • Experience with APIs, event-driven architectures, and integration patterns for cross-system data flows.
  • Familiarity with data catalog and metadata solutions: Amundsen, DataHub, Collibra, or Alation.

Soft Skills

  • Strong stakeholder management: translate technical constraints into business impact and align priorities across teams.
  • Excellent communication and documentation skills for runbooks, postmortems, and technical proposals.
  • Problem-solving mindset with emphasis on root cause analysis and long-term remediation.
  • Proactive ownership and bias for action—able to own incidents end-to-end and drive follow-up projects.
  • Collaboration and mentorship: coach junior engineers and promote best practices in reliability and testing.
  • Time management and prioritization in high-throughput, multi-stakeholder environments.
  • Adaptability to fast-changing product requirements and cloud-native operational patterns.
  • Attention to detail when validating schemas, lineage, and data contracts to prevent silent failures.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Engineering, or MBA with technical project experience preferred but not required.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 2–7 years in data engineering, SRE, or ETL/operations roles; mid-level roles typically require 3+ years.

Preferred:

  • 4–8+ years of hands-on experience operating production data pipelines, working with cloud data warehouses, and owning data reliability or platform responsibilities. Demonstrated experience with incident management, performance tuning, and cross-functional stakeholder engagement preferred.
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