Data Engineer II

About the Team
We’re looking for a Data Engineer II to join our Data Engineering team, which builds and governs the data foundation that powers the business. You’ll work within our stack — Python ingestion pipelines, Airflow orchestration, and Snowflake/Databricks — helping move data reliably and securely from source to decision-ready output.

This is an entry-level role. You’ll execute well-defined tasks under the direction of senior data engineers, learn our team’s stack and conventions, and build a strong foundation in pipeline correctness. You’re not expected to own designs independently yet — you’re expected to build reliable software against a design, ask good questions, and grow quickly from feedback.

Responsibilities
Implement ingestion pipelines and Airflow DAGs from a senior engineer’s design, using the team’s scaffolding and conventions — including writing the code, unit tests, and documentation
Support data security and governance work, such as PII masking and access controls, following established patterns
Contribute to data delivery work, including reverse ETL integrations, under guidance from senior engineers
Add and extend fields in existing pipelines, incorporating review feedback and applying learned patterns on future work
Take oncall pages for pipeline failures, work through runbooks, and escalate with clear context when needed
Pair with senior engineers on data integrity issues you can’t yet diagnose alone
Write clear, reviewer-friendly PR descriptions and ask clarifying questions before starting new work
Flag blockers early and with context rather than going quiet when stuck
Build strong working relationships with internal stakeholders (BI analysts, other data engineers, data scientists) and help gather and clarify requirements
Conduct and participate in code and system inspections
Help the team define and adhere to data engineering best practices
Mentor more junior data engineers as you grow into the role

Experience and Skills
1–3 years of professional software or data engineering experience
A self-learner with a strong ability to gather, evaluate, and analyze requirements
Solid foundation in Python and deep understanding of SQL and ETL/ELT for complex data transformations
Comfort reading and writing unit tests, and working within an established codebase and conventions
Familiarity with (or eagerness to quickly learn) workflow orchestration tools like Airflow (Managed Workflows for Apache Airflow)
Basic understanding of data pipeline concepts: ingestion, idempotency, scheduling, and data quality
Knowledge of several of the following technologies: Snowflake, Databricks, AWS, dbt, Tableau, MongoDB, PostgreSQL
Familiarity with Git-based version control and PR-based code review workflows
Strong communication skills — asks clarifying questions, writes clear PR descriptions, and escalates blockers with useful context rather than staying stuck silently
A growth mindset: takes review feedback well, improves processes, and champions best practices to avoid technical debt

Preferred But Not Required
Exposure to cloud data warehouses/lakehouses (Snowflake, Databricks, AWS) and data catalog/lineage tooling
Familiarity with dbt, Tableau, MongoDB, or PostgreSQL
Familiarity with reverse ETL tools or patterns (e.g., Segment, LaunchDarkly, Kafka, S3-based delivery)
Exposure to PII masking, data security, or RBAC/access governance concepts
Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting
Background in gaming, betting, e-commerce, or another regulated/high-compliance industry
Familiarity with responsible handling of customer/PII-sensitive data