Target.com - Lead Data Analyst

Lead Data Analyst

Job Description:

As a part of the Merchandising Analytics and Insights team, our analysts work closely with business owners as well as technology and data product teams staffed with product owners and engineers. They support all Merchandising strategic initiatives with data, reporting and analysis. Merchandising teams rely on this team of analysts to bring data to support decision making.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

- As a Lead Data Analyst your responsibilities will be exploring data, technologies, and the application of mathematical techniques to derive business insights. Data analysts spend their time determining the best approach to gather, model, manipulate, analyze and present data. You will lead an agile team which requires active participation in ceremonies and team meetings. In this role, you'll have opportunities to continuously upskill to stay current with new technologies in the industry via formal training, peer training groups and self-directed education.

- This role specifically will support a new initiatives requiring new data and metric development. Your curiosity and ability to roll up guest level insights will be critical. The team will leveraging in store analytic data to measure the impact of promotions, product placement, or physical changes at any given store has on store operations, guest experience and purchase decisions. This new capability are still being defined, which allows for creative thinking and leadership opportunities. Job duties may change at any time due to business needs.

Key Responsibilities:

- Work with MC, Merch, Planning, Marketing, Digital, etc. teams to use data to identify opportunities, bridge gaps, and build advanced capabilities

- Partner with Product, DS, DE, etc. to determine the best approach to gather, model, manipulate, analyze and present data

- Develop data and metrics to support key business strategies, initiatives, and decisions

- Explore data, technologies, and the application of mathematical techniques to derive business insights

Desired Skills & Experiences-:

- Ability to breakdown complex problems, identify root cause of the issue, and develop sustainable solutions

- Ability to influence cross-functional teams and partners at multiple levels of the organization

- Possess analytical skills (SQL, Python, R, etc.) to find, manipulate, and present data in meaningful ways to clients

- Desire to continuously upskill to stay current with new technologies in the industry via formal training, peer training groups and self-directed education

About you:

(In terms of technical):

- B.E/B.Tech, M.Tech, M.Sc. , MCA - Overall 8-12 years of experience. And 6-8 years data ecosystem experience.

- Strong Architect of data capabilities and analytical tools. Proven experience to architect enterprise level Data warehouse solutions and BI Implementations across Domo, Tableau & other Visualization tools.

- Provide expertise and ability to train and guide team to implement top design architectures to build next generation analytics

- Deep Big data experience. Should have solid experience in Hadoop ecosystem and its components around writing programs using Map-Reduce, experience in developing Hive and PySpark SQL and designing and developing Oozie workflows.

- Hands on experience in object oriented or functional programming such as Scala &/or Python/R or other open-source languages

- Strong foundational mathematics and statistics

- Experience in analytical techniques like Linear & Non-Linear Regression, Logistic Regression, Time-series models, Classification Techniques etc

(In terms of soft skills for lead role)

- Strong stakeholder management with product teams and business leaders.

- Has strong problem solving, analytical skills and ability to manage ambiguity.

- Ability to communicate results of complex analytic findings to both technical /non-technical audiences and business leaders.

- Ability to lead change, work through conflict and setbacks .

- Experience working in an agile environment (stories, backlog refinement, sprints, etc.).

- Excellent attention to detail and timelines. Strong sense of ownership.

- Desire to continuously upskill to stay current with new technologies in the industry via formal training, peer training groups and self-directed education