Senior Data Engineer – Data Foundations

Arlo Technologies•Milpitas, CA

1d•$160,000 - $200,000

About The Position

Arlo is seeking a Data Engineer with strong analytics and business insight to build trusted metrics, scalable data pipelines, and curated datasets used by both internal teams and external partners. This role focuses on transforming business logic, device telemetry, backend events, and AI-generated signals into trusted, decision-ready data products. Success requires strong judgment around metric definitions, data quality, and delivering partner-facing data with a customer-obsessed mindset.

Requirements

4-6+ years of experience as a Data Engineer, Analytics Engineer, or in a similar data-focused role.
Strong proficiency in SQL and experience building analytics-ready data models.
Hands-on experience with Databricks or similar cloud lakehouse platforms, including strong proficiency with SQL-based transformations.
Deep understanding of BI and analytics concepts, including metric definitions, dimensional modeling, and report-ready datasets.
Demonstrated ability to interpret existing reporting logic and translate it into scalable, production-grade pipelines.

Nice To Haves

Experience working with Tableau data sources (TDS/TDSX), published extracts, or equivalent BI artifacts.
Experience with ETL / orchestration tools such as Databricks Workflows, dbt, Airflow, or similar.
Familiarity with external or third-party data sharing technologies and requirements (e.g., Delta Sharing / DeltaShare or similar partner data export mechanisms).
Experience delivering data products to external customers or partners.
Background in SaaS, subscription, IoT, or consumer electronics environments.

Responsibilities

Design and maintain production-grade pipelines ingesting telemetry, backend events, and AI signals.
Translate legacy BI logic (e.g., Tableau sources) into governed SQL transformations.
Build ETL / ELT workflows in Databricks and ensure SLA-driven freshness and reliability.
Partner with the Data Analytics team to define and maintain core product, device, and service metrics.
Translate business requirements into ‘Bronze layer’ data ingests that support metric definitions through durable data models.
Build report-ready facts and dimension models and data pipelines to ensure semantic consistency.
Serve as technical contact for our partner data and product teams as required.
Work with Product, BI, AI/ML, Platform, and Finance to align data outputs to business needs.
Clearly communicate assumptions, risks, and tradeoffs.
Own end-to-end data correctness and alerting.
Implement validation, reconciliation, monitoring, and documentation standards.
Improve pipeline reliability, performance, and maintainability.
Reduce manual effort and contribute to data engineering best practices.