Senior Data Analyst

Qualifications & Responsibilities

The Analytic Consulting Group partners with internal and external clients and data providers, leveraging predictive analytics and advanced statistical techniques to drive strategic thought and effective decision making. The Sr. Data Analyst helps support all aspects of analytic initiatives from conception to completion.

Responsibilities:

Produce dashboards with Excel, Tableau, or other tools to provide routine reporting to clients and internal stakeholders. These dashboards will cover both program performance/business impact and activity monitoring to detect execution errors.

Analyze the delivery and performance of digital marketing channels.

Use Databricks, SQL, Python, Pyspark Excel, and other tools to conduct analysis of a client’s relationship marketing programs

In collaboration with other team members, design and execute ad hoc analyses to answer questions about program performance

Collaborate with internal and external stakeholders to manage data logistics - including data transfers, understanding data structures, business rules, etc. - to enable project execution

Contribute to client-facing deliverables presenting and explaining the results of analyses, and providing appropriate strategic and tactical recommendations

Appropriately account for the timeliness and quality of all assignments

QA/QC data and report outputs to ensure accuracy

Manage multiple projects concurrently

Minimum Qualifications:

Minimum 2 years of experience in marketing analytics field.

Minimum 2 years of hands-on experience using Databricks, SQL, Python, and PySpark in an analytics environment.

Proficient in Tableau or a similar data visualization tool

Solid planning, priority setting, and project management skills with experience managing multiple projects concurrently

Excellent verbal and written communication skills

Successful in developing relationships within and across functional teams (including statistical, technical, and marketing resources)

Highly motivated and collaborative

Strong technical skills

Acute attention to detail (QA/QC)

Effective organization and time management skills

Desirable Qualifications:

Preferred - Advanced degree (Master’s) in Statistics, Economics or other quantitative discipline

Ability to use additional data/statistical tools: Python, R, etc.

Support implementation of business intelligence and marketing automation solutions

Strong understanding of data hygiene, merge/purge, and transformations processes

Translate campaign requirements and implement SAS Code that interfaces with proprietary data orchestration tools for client data processing

Proven analytical and problem-solving abilities. Ability to quickly discern the most relevant facts and recommend the quickest, most practical, and safest approach to resolving an issue with a program

Attention to detail and strong logical/analytical skills with a keen ability to anticipate and avoid problems

Ability to effectively manage, prioritize, and execute several tasks at once within project deadlines

Produce dashboards with Excel, Tableau, or other tools to provide routine reporting to clients and internal stakeholders. These dashboards will cover both program performance/business impact and activity monitoring to detect execution errors.

Analyze the delivery and performance of digital marketing channels, including targeted display, search, and landing pages

Use SAS, Python, SQL, Excel, and other tools to conduct analysis of a client’s relationship marketing programs

In collaboration with other team members, design and execute ad hoc analyses to answer questions about program performance

Collaborate with internal and external stakeholders to manage data logistics - including data transfers, understanding data structures, business rules, etc. - to enable project execution

Appropriately account for the timeliness and quality of all assignments

QA/QC data and report outputs to ensure accuracy

Manage multiple projects concurrently

Able to decompose and solve problems at the appropriate level within the system, keeping in mind the higher-level structures and their requirements and dependencies with adjoining subsystems.