Data Engineer

Cerity Partners•New York City, NY

12d

About The Position

We are looking for someone whose mindset is about data models and who speaks fluent SQL to join our data team. As a data engineer at Cerity Partners you will play a leading role maintaining the current--and architecting the future--state of our data pipelines, analytics, and reporting. Data is what empowers our advisors, operations, and management to provide exceptional service to our clients and to continue our growth as one of the leading RIA firms in America.

Requirements

7+ years of experience as a data engineer
5-10+ years of programming experience
PostgreSQL, Python, dbt, Airflow, Git, advanced SQL
REST APIs (GraphQL a plus)
Cloud infrastructure expertise (particularly around managed databases)
Multiple years of experience with financial services data models (wealth management preferred)
CRM experience (Salesforce preferred)
PowerBI experience useful
Ability to work independently with a geographically distributed team
Acute attention to detail
Love of problem solving and being challenged
Willingness to try new things
Strong communication skills
Applicants must be authorized to work for any employer in the U.S.

Nice To Haves

GraphQL a plus
wealth management preferred
Salesforce preferred

Responsibilities

Design Data Models: Structure data and create database schemas that capture data as it exists and as it relates to other data.
Build Data Pipelines: Create, maintain, and optimize data pipelines and data sets.
Data Integration: Extract, transform, and push data to other systems and BI tools.
Database Management: Help build our database infrastructure and maintain it so that the organization’s data remains available and our systems performant.
Optimization: Identify and replace data models and queries that lead to performance issues. Fix issues with systems and data that lead to downtime or unexpected behavior.
Collaboration: Work with other stakeholders to develop and deliver the data they need to be successful.
Data Governance: Proactively identify data that is sensitive and take steps to address its storage and usage.
Data Quality: Data comes from many different systems and in many different formats. Build internal tooling and reporting to identify bad data, and gaps in data, so that end users can work with the data without worry.