Analytics Engineering Manager - Phaxis LLC
New York, NY
About the Job
We're looking for an Analytics Engineering Manager, Vice President to measurably drive growth . This role would involve managing a team of analytics engineers, working with business stakeholders to defineproject requirements , and finally developing and executing on implementation plans.
This role involves both management and individual contribution. You will be expected to work with various department leads (e.g. sales, marketing, finance, product, operations), understand their pain points and business processes, and ultimately develop data-driven solutions to solve their needs.
These solutions span data ingestion, centralization, governance, business intelligence and machine learning. While you will have a team to assist you, you are also expected to be heavily involved in all technical details of the project. While not all of our work directly involves software engineering, almost all of it is heavily informed by software engineering principles.
The actual outputs of our work can be: clean and well-governed data models, management dashboards, exploratory/statistical data analyses (with attendant recommendations), documentation, machine learning models, or data apps.
You will sit in the Analytics group of the broader Data Office team and work closely with our Data Engineering and Data Science teams. On the technical side, you will interface with our entire data stack of Airbyte, Snowflake, dbt, Prefect, Python, Tableau and SageMaker (among others). On the business side, you will work with our sales, marketing, operations, product and finance teams to drive top-line growth metrics (e.g. sales, usage, conversion).
This is a technical role where you will be writing code. Ideal candidates will be able to understand complex business problems, interface with senior stakeholders, develop execution plans, implement all aspects of the project technically, and finally present on their work.
Responsibilities:
Manage of team of analytics engineers in project scoping, unblocking technical challenges and ensuring timely delivery of work
Drive projects to completion by gathering business requirements, implementing technical solutions, following software engineering best practices, and finally presenting on results
Write Python and SQL (in dbt) to extract, transform, validate, and aggregate data
Create Tableau dashboards for various business teams, charting key metrics and performing exploratory data analysis
Develop statistical models, machine learning models and construct data-driven experiments
Convert data insights into concrete, action-oriented and phased execution plans that measurably grow various business metrics over time
Work closely with our engineering, product and business teams to form a thorough understanding of our industry and evolving data model
Required Qualifications:
Bachelor's degree or higher in Computer Science, Economics, Mathematics, Statistics or a related technical field
5 to 9 years of experience in a data-related role
Excellent writing, communication and presentation skills
Excellent knowledge of SQL (dbt experienced preferred)
Excellent knowledge of Python, Pandas and statistics
Excellent knowledge of data modeling, relational databases, normalization, OLAP stores
Good knowledge of data science techniques and machine learning models
Good knowledge of a business intelligence tool (e.g. Tableau, Looker, PowerBI)
Good knowledge of data governance principles, data stewardship, traceability, lineage
This role involves both management and individual contribution. You will be expected to work with various department leads (e.g. sales, marketing, finance, product, operations), understand their pain points and business processes, and ultimately develop data-driven solutions to solve their needs.
These solutions span data ingestion, centralization, governance, business intelligence and machine learning. While you will have a team to assist you, you are also expected to be heavily involved in all technical details of the project. While not all of our work directly involves software engineering, almost all of it is heavily informed by software engineering principles.
The actual outputs of our work can be: clean and well-governed data models, management dashboards, exploratory/statistical data analyses (with attendant recommendations), documentation, machine learning models, or data apps.
You will sit in the Analytics group of the broader Data Office team and work closely with our Data Engineering and Data Science teams. On the technical side, you will interface with our entire data stack of Airbyte, Snowflake, dbt, Prefect, Python, Tableau and SageMaker (among others). On the business side, you will work with our sales, marketing, operations, product and finance teams to drive top-line growth metrics (e.g. sales, usage, conversion).
This is a technical role where you will be writing code. Ideal candidates will be able to understand complex business problems, interface with senior stakeholders, develop execution plans, implement all aspects of the project technically, and finally present on their work.
Responsibilities:
Manage of team of analytics engineers in project scoping, unblocking technical challenges and ensuring timely delivery of work
Drive projects to completion by gathering business requirements, implementing technical solutions, following software engineering best practices, and finally presenting on results
Write Python and SQL (in dbt) to extract, transform, validate, and aggregate data
Create Tableau dashboards for various business teams, charting key metrics and performing exploratory data analysis
Develop statistical models, machine learning models and construct data-driven experiments
Convert data insights into concrete, action-oriented and phased execution plans that measurably grow various business metrics over time
Work closely with our engineering, product and business teams to form a thorough understanding of our industry and evolving data model
Required Qualifications:
Bachelor's degree or higher in Computer Science, Economics, Mathematics, Statistics or a related technical field
5 to 9 years of experience in a data-related role
Excellent writing, communication and presentation skills
Excellent knowledge of SQL (dbt experienced preferred)
Excellent knowledge of Python, Pandas and statistics
Excellent knowledge of data modeling, relational databases, normalization, OLAP stores
Good knowledge of data science techniques and machine learning models
Good knowledge of a business intelligence tool (e.g. Tableau, Looker, PowerBI)
Good knowledge of data governance principles, data stewardship, traceability, lineage
Source : Phaxis LLC