Data Engineer

As a Data Engineer, you will be an important part of the Enterprise Business Unit (EBU) team responsible
for owning and maintaining the data pipeline layer that powers reporting for our platforms. Reporting is
critical to both internal operations and external clients. If you have experience maintaining and improving
data pipelines, working with orchestration tools, and ensuring data quality in a production environment,
read on.

Requirements

4+ years of experience in data engineering
Strong Python — ETL scripting and pipeline development

Apache Airflow — author, maintain, and troubleshoot DAGs across a 223-DAG multi-domain
production environment; KubernetesExecutor experience a plus
dbt (dbt-bigquery preferred) — model authoring, schema tests, source freshness
Strong SQL and BigQuery proficiency
S3 data pipeline experience — reading, writing, and managing partitioned data
Familiarity with Spark or PySpark for large-scale data ingestion
GitHub Actions CI/CD
Solid understanding of ETL/ELT design patterns, data warehousing concepts, and data quality
practices
Nice to haves:
dbt best practices — incremental models, snapshots, macros
Ad-tech reporting pipeline experience — impressions, clicks, attribution, pixel firing
Databricks familiarity (consumer/reporting side)
SFTP-based data delivery patterns
IKS or Kubernetes deployment experience
Scala familiarity
React or frontend development experience

An owner — if data is broken, that’s your problem to fix, not just flag
A reliable maintainer — you respect production systems, assess risk before making changes, and
don’t break things trying to improve them
A clear communicator — you can explain pipeline issues and tradeoffs to a non-engineering
audience
Comfortable with tech debt — you know how to sequence priorities and reduce risk without a full
rewrite
Quality-minded — schema tests and CI gates aren’t optional to you
A team player — you support your teammates, participate in meetings, and surface blockers early

Job responsibilities

Maintain and improve scheduled data pipelines handling reporting, attribution, pixel processing,
and product feed ingestion
Support orchestration layer (Airflow) — monitor DAGs, resolve failures, and maintain pipeline
health through instrumentation and monitoring across a large multi-domain DAG environment
Maintain and extend dbt transformation models in BigQuery
Ensure data quality through schema tests, source freshness checks, and CI gates
Available for production pipeline incidents outside of business hours
Execute tech debt remediation items — runtime upgrades, CI/CD migrations, and dependency
updates — in a prioritized backlog with some time-sensitive deadlines
Collaborate with backend engineers, the Director of Engineering, and the broader Data Platform
team
Troubleshoot and resolve data issues across pipeline, transformation, and reporting layers
Participate in an agile environment with two-week sprints and regular standups