Agentic Data Engineering

Agentic Data Engineering

Infosys / Apple

Pay rate: $55/hr on W2

Location : Sunnyvale Austin Raleigh

Webex hire

Responsibilities:

Understand the data needs of stakeholder teams in terms of key data models and reporting and translate that into technical requirements

Define build and manage key data pipelines in dbt that transform raw logs into canonical datasets

Establish high data integrity standards and SLAs to ensure timely accurate delivery of data

Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company

Build foundational data products dashboards and tools to enable self-serve analytics to scale across the company

Influence the future roadmap of Product and GTM teams from a data systems perspective

Become an expert in our organizations data models and the companys data architecture

You might be a good fit if you have:

5 years of experience as an Analytics Data Engineer or similar Data Science & Analytics roles preferably partnering with GTM and Product leads to build and report on key company-wide metrics.

A passion for the companys mission of building helpful honest and harmless AI.

Expertise in building multi-step ETL jobs building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub.

Expertise in SQL and Python to transform data into accurate clean data models.

Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams.

A bias for action and urgency not letting perfect be the enemy of the effective.

A full-stack mindset not hesitating to do what it takes to solve a problem end-to-end even if it requires going outside the original job description.

Experience building an Analytics Data Engineering (or similar) function at start-ups.

A strong disposition to thrive in ambiguity taking initiative to create clarity and forward progress.