Analytics Engineer

Analytics Engineers are multidiscipline individuals whose skills sit at an intersection of Business Teams, Data Analytics, Data Science, and Data Engineering responsible for bringing robust, efficient, and integrated data models and analytic solutions to life. Analytics Engineers possess a high level of business acumen, as well as technical mastery, can speak in business terms, and are able to translate data insights and analysis needs into analytic models. Analytics Engineers thrive at being able to blend business acumen with technical expertise and transition between business strategy and data development.

Analytics Engineers partner closely in a highly collaborative environment working with divisional & departmental leaders across Shaw’s Residential Sales and Marketing as well as Shaw’s Data Management Team to provide data driven analytical solutions which leads to business value, decision enablement, and behavior-based outcomes.

As members of the Residential Enablement team, Analytics Engineers will be responsible for coaching & mentoring within Shaw’s business units to further enable self-service analytics across Shaw and provide governance over best practices for data visualization & analytic related activities.

Job Responsibilities:

Collaborate & Lead:
- Collaborate, consult, & lead discussions with senior leaders, directors, & territory managers as well as business area analysts to collect business requirements, define successful analytic outcomes, and design supporting data models.
- In this Senior role, your time will be spent leading, influencing, coaching, and facilitating discussions which evolves and advances analytic solutions.
- Partner closely with Business Leads to consult on Advanced Analytic capabilities and provide innovative approaches towards analytic solutions.
- Lead in the Residential Analytics Engineering & Support team to coach, influence, and provide analytic governance to drive best practices and development standards across the enterprise.
- Works closely with Data Engineering to build and maintain complex databases as well as streamline processes to ensure that data is cleaner earlier in the data integration flow process.
Organize and Transform Data:
- Model, develop, design, optimize, and maintain production level reporting & analytic solutions using tools such as Power BI, R, & Python to enable data mining & analytic capabilities.
- Develop, implement, and champion data & analytical development best practices to ensure data accuracy and trust in analytical solutions.
- Additional time will be spent as a lead developer working with an advanced analytics team executing analytic solutions via the analytics development lifecycle.
- Lead & execute at least 2 or more large analytic projects with a duration of 12+ months, which also span multiple business areas within Residential Sales and Residential Marketing.
- Design, develop, optimize, and maintain data architectures using advanced SQL query or Python scripting methods (within dataflows or views in Databricks).
- Conducts data cleansing and validation exercises to ensure quality of data and analytic results.
- Apply machine learning principles, development process, and implementation within applicable business requirements on Power Platform or Databricks
- Maintain the Data Catalog, Data Lineage, and Data Assets that support Enterprise Data Management.
- Continuously Learn
- Displays a genuine interest in developing business acumen and understanding of key business drivers that are used in the development of analytic solutions.
- Continually learn about Data Visualization, Machine Learning, Data Science, AI, Statistics, and other Advanced Analytics concepts.

Qualifications:

Advanced degree in Business Analytics, Mathematics, Statistics, Computer Science, or a related field.
5+ years of experience providing data engineering, data visualizations, and / or implementing descriptive, predictive, and prescriptive analytics solutions.
Strong understanding of PowerBI, SQL, R, Python, & Azure
Travel may be required for conference attendance.
Additional Requirements:
- Demonstrated ability to successfully coach, mentor, and lead other Analytics Engineering teams and team members.
- Strong ability to collaborate in a cross functional environment with IT and various business users.
- Conduct data code reviews with other team members and offer constructive guidance on solution development.
- Provides mentorship to help team members grow their technical and business capabilities.
- Creates and refines Data & Analytic Governance and Best Practices guidelines.
- Demonstrated experience leading multiple analytics projects from beginning to operationalization.
- Demonstrated experience developing design, reference, or architectural diagrams to be consumed by others for developmental accuracy.
- Demonstrated proficiency with data system design, databases, schemas, data marts, aggregates, and views.
- Experience introducing a new tool or technique to a multi-person team, leading to measurable productivity improvement.
- Experience creating data pipelines in support of near real-time event stream processing.
- Possess a willingness to experiment and to confront the hardest and most complex problems.

Knowledge, Skills, and Abilities:

Ability to navigate ambiguity and quickly gain and apply understanding of business concepts to analytical solutions.
Understands overall Enterprise Data Architecture and demonstrates the ability to model complex data structures logically and physically to support analytical requirements.
Proven track record of independent thinking and innovative approaches to problem solving.
Excellent written, oral, and presentation skills.
Demonstrated ability to manage concurrent initiatives.
Understands the full lifecycle of analytic projects and DataOps concepts.
Ability to present ideas in a meaningful, business minded way, to diverse audiences.
Strong analytical and problem-solving abilities to deliver root cause analysis.
Strong customer service orientation.
Experience working in a team oriented, collaborative environment.
Ability to communicate technical information effectively to business users.

Preferred Technical Skills:

Strong SQL / ETL / Database Knowledge
Strong PowerBI development and UX design.
Experience with Data Science tools such as R or Python.
Experience with Data Lakes and tools such as Databricks.

Required Competencies:

Deliver Compelling Communication
Build Customer Orientation
Learn Continuously
Build Trusting Relationships
Coach & Support
Innovate
Demonstrate Strategic Influence
Coach & Develop Others

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