About the Role
As a Data Engineer II on the Data team, you will own meaningful slices of our data platform end-to-end from ingestion through transformation to the data products that power decision-making across the company. You'll design and operate batch and real-time pipelines, partner closely with Data Scientists, Analysts, and business stakeholders, and raise the bar on craftsmanship, reliability, and developer productivity for the team. You'll work in a modern, AI-augmented engineering environment: shipping faster with Claude Code and other AI coding assistants, building AI-powered internal tools (natural-language-to-SQL, automated data quality, lineage, anomaly detection), and bringing GenAI thoughtfully into data workflows. We expect you to take significant projects from ambiguous problem statement to delivered outcome, mentor newer engineers, and represent the team confidently with cross-functional partners.
What You Will Do
- Design, build, and operate efficient, reliable, and well-documented data pipelines across batch and streaming systems from source ingestion through the warehouse to consumer-facing data products.
- Own the data models, SLAs, and quality contracts for the domains you cover; treat documentation, testing, and observability as first-class deliverables.
- Improve the data platform itself: introduce or extend tooling, templates, CI/CD, testing frameworks, and monitoring that make the entire team faster and more reliable.
- Adopt and champion AI-assisted engineering use Claude Code and similar tools as part of your daily workflow, build internal AI-powered utilities (text-to-SQL on the semantic layer, automated PR review, log triage, data discovery), and evaluate where LLMs add durable value vs. hype.
- Partner directly with Data Scientists, Analysts, and business stakeholders to translate ambiguous requirements into well-scoped deliverables; communicate trade-offs, anticipate push-back, and drive alignment without needing escalation.
- Mentor interns, new hires, and L2 engineers through onboarding, code review, design feedback, and pairing.
- Contribute to cloud infrastructure and governance: IAM, service accounts, secrets management, cost optimization, and Terraform-managed GCP / Confluent Cloud resources.
- Drive incident response and root-cause analysis for the pipelines you own; close the loop with durable fixes, runbooks, and prevention.
Required education
Bachelor's Degree
Preferred education
Master's Degree
Required technical and professional expertise
- 4 to 7 years of experience in Data Engineering at a technology company, including production ownership of non-trivial pipelines and data models.
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
- Strong command of advanced SQL and Python you can write production-grade code, review others' code, and pick the right tool for the job.
- Solid grounding in dimensional modeling, data warehousing, and ELT/ETL fundamentals; you can design a clean star schema and explain the trade-offs.
- Hands‑on experience operating orchestration frameworks in production (Airflow / Cloud Composer, Dagster, or similar).
- Hands‑on experience building streaming and event-driven pipelines Kafka, Flink, or equivalent and reasoning about exactly‑once, schema evolution, and back‑pressure.
- Experience on a major cloud platform (GCP preferred) and with infrastructure‑as‑code (Terraform).
- Demonstrated ability to operate with moderate ambiguity: scope your own work, identify what to clarify, recommend an approach, get buy‑in, and ship.
- Fluency with AI coding assistants (Claude Code, Cursor, Copilot) as a daily tool and judgment about where they help vs. where they hurt.
- Clear written and verbal communication; comfortable presenting designs, post‑mortems, and trade-offs to technical and non-technical audiences.
Preferred technical and professional experience
- Production experience with dbt (or SQLMesh) and the surrounding ecosystem testing, packages, semantic layers (dbt Semantic Layer, Cube, Hex).
- Experience with open table formats (Apache Iceberg, Delta Lake) and modern lakehouse patterns.
- Familiarity with data quality / observability tooling (Great Expectations, Soda, Elementary, Monte Carlo) and catalog / lineage systems (DataHub, Atlan, Unity Catalog).
- Experience building or integrating with AI/ML infrastructure: feature stores (Feast, Tecton), vector databases (pgvector, Pinecone, Weaviate), or RAG pipelines for internal knowledge systems.
- Exposure to MLOps / LLMOps practices evaluation harnesses, prompt versioning, offline/online metric tracking.
- Experience contributing to or operating a semantic layer that powers self‑serve analytics and natural‑language interfaces.
- Familiarity with Confluent Cloud, Kafka Connect, and Flink SQL.
- Open‑source contributions or technical writing in the data engineering space.
Skills
Advanced SQL, Python, Dimensional Modeling, Data Warehousing (BigQuery preferred), dbt, Airflow / Cloud Composer, Kafka & Flink (or equivalent streaming), Terraform, GCP, CI/CD for data, Data Quality & Observability, AI-assisted development (Claude Code), applied GenAI for data workflows (text-to-SQL, RAG, agents). Years of Experience: 4 - 7