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roadmap updated 2026-06-01

DataOps Engineer Roadmap

Apply DevOps principles to data pipelines — CI/CD for data, data quality testing, pipeline observability, and data catalog management. Build reliable, testable, and observable data infrastructure.

Phase 1 — Beginner

Understand data engineering fundamentals, SQL, and how to build and test basic batch data pipelines.

dbtPythonSQLAirflowBigQuery

Phase 2 — Intermediate

Implement CI/CD for data pipelines, automated data quality testing, lineage tracking, and pipeline observability.

Apache AirflowdbtKafkaDataHubGreat Expectations

Phase 3 — Advanced

Architect enterprise DataOps platforms with federated governance, data mesh principles, and real-time data product delivery.

DatabricksSnowflakedbt CloudMonte CarloSoda

The path: Beginner → Intermediate → Advanced

Beginner

Focus: Understand data engineering fundamentals, SQL, and how to build and test basic batch data pipelines.

Skills to build

  • SQL fundamentals: joins, window functions, CTEs
  • Python data engineering: Pandas, PySpark basics
  • Data pipeline patterns: batch, micro-batch, streaming
  • Introduction to dbt for data transformation
  • Data warehouse concepts: star schema, fact and dimension tables
  • Git version control for SQL and pipeline code
  • Data quality testing concepts: nulls, uniqueness, referential integrity
  • Cloud data warehouses: BigQuery, Snowflake, or Redshift basics

Tools to learn

  • dbt
  • Python
  • SQL
  • Airflow
  • BigQuery
  • Git

Intermediate

Focus: Implement CI/CD for data pipelines, automated data quality testing, lineage tracking, and pipeline observability.

Skills to build

  • dbt testing and documentation as code
  • CI/CD pipelines for dbt models with GitHub Actions
  • Data lineage tracking with OpenLineage and Marquez
  • Pipeline orchestration with Airflow or Prefect
  • Data catalog integration with DataHub or Apache Atlas
  • Streaming pipelines with Kafka and Flink or Spark Streaming
  • Pipeline SLAs: freshness checks, row count anomaly detection
  • Infrastructure as code for data infrastructure with Terraform

Tools to learn

  • Apache Airflow
  • dbt
  • Kafka
  • DataHub
  • Great Expectations
  • OpenLineage
  • Terraform

Advanced

Focus: Architect enterprise DataOps platforms with federated governance, data mesh principles, and real-time data product delivery.

Skills to build

  • Data mesh architecture: data products, federated governance
  • Real-time streaming architecture with exactly-once semantics
  • Multi-cloud data platform design and cost optimization
  • Data contract design and enforcement between producers and consumers
  • Advanced pipeline observability: circuit breakers and automated remediation
  • Data platform reliability: SLOs for data freshness and quality
  • Column-level lineage and impact analysis for schema changes
  • DataOps culture: data team DevOps maturity and self-service enablement

Tools to learn

  • Databricks
  • Snowflake
  • dbt Cloud
  • Monte Carlo
  • Soda
  • Prefect
  • Iceberg

Labs to practice

Interview questions to prepare

  1. What is DataOps and how does it differ from traditional data engineering?
  2. How do you implement CI/CD for dbt models, including automated tests?
  3. What is data lineage and why is it critical for data governance?
  4. How do you detect and alert on data quality issues in a production pipeline?
  5. Explain the data mesh concept and what a ‘data product’ means in practice.
  6. How would you design a data contract between a data producer and consumer team?
  7. What is the difference between data freshness, completeness, and accuracy as data quality dimensions?
  8. How do you handle schema evolution in a data pipeline without breaking downstream consumers?

Certification suggestions

  • dbt Analytics Engineering Certification — dbt Labs
  • Databricks Certified Data Engineer Associate — Databricks
  • Google Professional Data Engineer — Google Cloud
  • AWS Certified Data Analytics – Specialty — Amazon Web Services
  • Snowflake SnowPro Core Certification — Snowflake

See exam formats, costs and official links in the certification registry.

Free resources

Portfolio project ideas

  • Build a dbt project on BigQuery with source freshness tests, schema tests, and a GitHub Actions CI pipeline that runs tests on every PR
  • Create an end-to-end streaming pipeline from Kafka to Snowflake using Flink with row count and latency SLO monitoring
  • Implement a data lineage graph using OpenLineage with Airflow DAGs and visualize dependencies in Marquez
  • Design a data product with a published data contract, automated quality checks, and a data catalog entry in DataHub

Mistakes to avoid

  • Not version controlling SQL and pipeline code — all transformations should live in Git, not a BI tool’s UI
  • Skipping data quality tests in CI — bad data flowing silently into downstream dashboards erodes trust
  • Hard-coding pipeline dependencies instead of using a scheduler — manual orchestration doesn’t scale beyond a few pipelines
  • Ignoring data lineage until an incident — without lineage, tracing the source of bad data can take days
  • Treating data schema changes as non-events — unannounced schema changes break downstream consumers and should follow a change management process

Keep going