Senior Data Engineering Developer II

Summary

We are looking for a Lead Data Engineer who is genuinely hands-on, sets the technical direction for the squad, and is comfortable being accountable for delivery without owning formal people-management. You will run the squad day to day, assigning and reviewing work, unblocking engineers, operationalizing data products, and working directly with the end users who depend on them. You will partner closely with the Data Engineering manager, who holds manager-of-record responsibility, while you own the engineering outcomes. You set the bar, you delegate with confidence, and you are accountable for what the team ships.
This is a leadership role for someone who still wants to be in the code, and who wants to be at the center of the AI build-out rather than watching it from the sidelines. If you want to architect a data product, write a meaningful part of it, then hand the rest off cleanly and hold the team to a high bar, this role is built for you.

Key Responsibilities

Your core mission is to power the AI data fabric: deliver the high-quality, well-governed, AI-ready data that AI and analytics teams build on, and partner with them to provide the fabric, features, and serving patterns they need. Alongside that, you own our data products end to end, working directly with the users who depend on them to turn one-off datasets into durable, self-serve products that are documented, governed, and trusted.
You are the technical owner for the squad’s Data Fabric and Data Products streams. You set the architecture and stay in the code: writing and reviewing production work, designing the data models, and setting the standard in BigQuery, Dataflow, and Kafka-based pipelines, while driving the ongoing modernization of legacy workloads onto the cloud.
You run the squad day to day: planning the work, delegating with intent, and owning delivery, quality, and the path back when commitments are at risk, while mentoring engineers and growing a technical lieutenant. You also own operational health, including monitoring, incident response, and on-call, and you steadily drive down toil through better observability and automation.

Critical Qualifications

• 7+ years of hands-on data engineering experience, or 10+ years in software engineering with substantial data exposure.
• Strong hands-on data engineering experience building and operating production data pipelines and data products at scale.
• Deep expertise with a modern cloud data stack. GCP (BigQuery, Dataflow) strongly preferred; equivalent AWS or Azure experience considered.
• Proficiency in SQL and at least one of Python, Java, or Scala for data engineering.
• Solid data modeling and warehouse design fundamentals, including building for performance, cost, and maintainability at scale.
• Experience with pipeline orchestration and scheduling (Airflow, Cloud Composer, or equivalent).
• Strong software engineering discipline: version control, code review, testing, and CI/CD applied to data pipelines.
• Demonstrated technical leadership: leading the design and delivery of multi-person initiatives, reviewing others’ work, and setting engineering standards.
• Experience with streaming and event data (Kafka or equivalent) and with infrastructure as code (Terraform).
• A track record of operational ownership: on-call, incident response, monitoring, and reliability engineering for data or backend systems.
• A working grasp of data quality, lineage, and governance, and a habit of building it in rather than bolting it on.
• The judgment and communication skills to delegate, hold people accountable, work directly with end users, and manage stakeholders without formal authority over them.

Preferred Qualifications

• Experience building data foundations for AI or machine learning: feature pipelines, vector or embedding stores, RAG data layers, or ML serving.
• Hands-on use of AI tooling in the engineering workflow (code assistants, agentic tooling, LLM-based data quality or enrichment).
• Experience with transformation frameworks and a semantic or metrics layer (dbt or equivalent).
• Experience migrating workloads from on-prem Hadoop or Spark to a cloud data platform.
• Familiarity with telecom or network telemetry data.
• Experience working in an Agile or SAFe delivery model, including PI planning.
• Exposure to data governance, privacy, or regulatory reporting.
• Familiarity with observability tooling (Grafana and similar).
• Experience with containerization and orchestration (Docker, Kubernetes).
• Cloud cost management or FinOps awareness for data platforms.
• A relevant degree or cloud certification (for example GCP Professional Data Engineer).

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