Data Engineer

Job Description

School Degree


A Data Engineer is responsible for designing, developing, producing and owning a specific business domain s core models. These data models are often intended to be used not only by members of that business domain, but also data consumers from across the company. Common uses of this data include metrics generation, analytics, experimentation, reporting, and ML feature generation.

Data Engineers do not build datasets for individual applications (e.g. a report, a metric, an analysis) or for experimental nascent metrics. Application-specific datasets are typically built by the application owner, often using one or more core data models as inputs.

The responsibilities of a data engineer by development phase include:


  • Identify and gather the most frequent data consumption use cases for the datasets they are designing. A critical question we expect DEs to weigh in is whether existing data models can satisfy (or at least be used as foundation) prior to building new data.
  • Understand the impact of each requirement and use impact to inform prioritization decisions
  • Define data governance requirements (data access privacy PII, retention etc.)
  • Define requirements for upstream data producers to satisfy intended data access patterns (including latency and completeness)


  • Guide product design decisions to ensure the need for data timeliness, accuracy, and completeness are addressed
  • Design the data set required to support a specific business domain (i.e. the data model for that business domain)
  • Identify required data to support data model requirements
  • Work closely with online system engineers to influence the design of online data models (events and production tables) such that they meet the requirements of their offline data
  • Partner closely with data source owners (i.e. engineers and APIs and their owners) to specify and document data required to be ingested for the successful delivery of a requirement
  • Define and own schema of events for ingested data
  • Define and document data transformations that will need to be made to transform ingested data into data warehouse tables
  • Validate the data model meets
  • Validate the data model integrates with adjacent data models
  • Document tables columns for data consumers
  • Optimize data pipeline design to ensure compute and storage costs are efficient over time


  • Implement data pipelines (streaming batch) to execute required data transformations to meet design requirements
  • Validate incoming data to identify syntactic semantic data quality issues
  • Validate data through analysis and testing to ensure the data produced meets the requirements specifications
  • Implement sufficient data quality checks (pre-checks, post-checks, anomaly detection) to preserve ongoing data quality
  • Partner with data consumers to validate resulting tables address the intended business need
  • Provide and solicit constructive peer review on data artifacts like pipeline design, data models, data quality checks, unit tests, code etc.


  • Continually improve, optimize and tune their data pipelines for performance, storage cost efficiency. Simplify existing systems and minimize complexity. Reduce data technical debt.
  • Actively deprecate low usage, low quality and legacy data and pipelines.
  • Triage and promptly correct data quality bugs. Implement additional data quality checks to ensure issues are detected earlier
  • Invest in automation where possible to reduce operational burden

Foundations, Citizenship Stewardship

  • When building a tool to improve general DE workflow, contribute to development of tools to support company-wide instead of local one-off solutions
  • Contribute to education of other DEs and data consumers on the data they curate for the business
  • Be data driven and influence data driven decisions. Be factual in communication, use data to effectively to tell stories, be critical of decisions not founded on data
  • Actively participate in recruiting, interviewing, mentoring and coaching. Champion the mission of Data Engineering by representing Airbnb at tech talks, blog posts, conferences, data meetups and communities
What Is A Data Engineer Not Responsible For
  • Implementing and ensuring the syntactic and semantic quality of events sent to the offline data ecosystem. This is owned by the online system s engineering team or the Data Platform team (depending on the ingestion mechanism).
  • Developing artifact-specific datasets for reports or models - generally owned by Data Science or Analysts through company
  • Developing or maintaining Minerva assets - generally owned by Data Science
  • Developing or maintaining reports dashboards - generally owned by Data Science or Analysts throughout company
  • Developing maintaining specific ML features - generally owned by Data Science or ML Engineers throughout company

Not every Data Engineer will require all of these skills, but we expect most Data Engineers to be strong in a significant number of these skills to be successful at Airbnb.

  • Data Product Management
  • Effective at building partnerships with business stakeholders, engineers and product to understand use cases from intended data consumers
  • Able to create maintain documentation to support users in understanding how to use tables/columns
  • Data Architecture Data Pipeline Implementation
  • Experience creating and evolving dimensional data models schema designs to structure data for business-relevant analytics. (Ex: familiarity with Kimballs data warehouse lifecycle)
  • Strong experience using ETL framework (ex: Airflow, Flume, Oozie etc.) to build and deploy production-quality ETL pipelines.
  • Experience ingesting and transforming structured and unstructured data from internal and third-party sources into dimensional models.
  • Experience with dispersal of data to OLTP (ex: MySQL, Cassandra, HBase, etc) and fast analytics solutions (ex: Druid, ElasticSearch etc.).
  • Data Systems Design
  • Strong understanding of distributed storage and compute (S3, Hive, Spark)
  • Knowledge in distributed system design, such as how map-reduce and distributed data processing work at scale
  • Basic understanding of OLTP systems like Cassandra, HBase, Mussel, Vitess etc.
  • Coding
  • Experience building batch data pipelines in Spark
  • Expertise in SQL
  • General Software Engineering (e.g. proficiency coding in Python, Java, Scala)
  • Experience writing data quality unit and functional tests.
  • (Optional) Aptitude to learn and utilize data analytics tools to accelerate business needs
  • (Optional) Stream Processing:
  • Experience building Stream Processing jobs on Apache Flink, Apache Spark Streaming, Apache Samza, Apache Storm or similar streaming analytics technology.
  • Experience with messaging systems (ex: Apache Kafka or RabbitMQ etc.)
  • Experience designing and implementing distributed and real-time algorithms for stream data processing.
  • Understand concepts of schema evolution, sharding, latency etc.
  • Good understanding of Lambda Architecture, along with its advantages and drawbacks

UG: Any Graduate - Any Specialization
PG: Any Postgraduate - Any Specialization

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