Analytics Engineer

Analytics engineers understand the fundamentals about how data can be modeled, explored, and interpreted to derive actionable insights from data, answer business questions, and support strategic decisions for our clients. 

Because our clients are mostly US-based organizations, we look for the ability to communicate with professional proficiency in English, verbally and in writing.

Functional Skills & Knowledge: You are responsible for modeling and transforming data from the source so that it can be easily analyzed, visualized, and acted upon by the data analyst or business user. You are capable of manipulating data through data cleaning, data conversion, and data modeling. Your job is ensuring that data is ingested, transformed, documented, and ready to be used for analytics.  
•    Designs data models, implements data governance, selects and implements transformation and analytics tools, and creates efficient data querying and processing methods. 
•    Works with tools like business intelligence (BI) platforms and data visualization tools. 
•    Has a solid understanding of discipline basics, wherein you can competently execute well-scoped tasks or product improvements with the guidance of senior engineers and management. You can consistently deliver small features and improvements that have an impact on clients. 
•    Provides constructive feedback on peer code or technical design document reviews 
•    With career progression, you will increasingly do the following: 
o    Participate in design and bug fixes under direct supervision. Writes, tests, and documents code as per company’s guidelines, writing and reviewing technical design documentation. 
o    Confidently contribute to internal analytics projects, eventually improving our in-house approach by regularly planning technical work with the team. 
o    Independently lead small- to medium-sized projects (4-8 weeks) through the entire lifecycle from design to development and deployment. 
o    Serve as a mentor for less advanced team members and onboarding new engineers onto the team. 
 
Cross-Team Collaboration: You are responsible for collaborating with peers and senior ICs in other functional departments—most often data solutions, data analytics, and data engineering—to develop and implement analytics engineering approaches that support our client engagement goals. 
•    Effectively collaborates across disciplines, including data engineering and analytics. 
•    Collaborates with data analysts and data engineers to ensure that the data infrastructure supports analytics needs. 
•    Approaches problem-solving as a team effort, providing timely and helpful feedback to peers and leads.  
•    Works well with other members of the team and are comfortable challenging other people’s opinions and open to having their own opinions challenged, too. 
•    Regularly seeks feedback from other team members (other Engineers, Data Solutions Managers, and peers in other functions) and incorporates that feedback in day-to-day work. 
  
Engagement Delivery: You are responsible for ensuring that smaller and/or lower complexity analytics engineering tasks are delivered on time and within scope. 
•    Consistently seeks out, delivers and maintains small projects or surface-specific tasks with the help of more experienced Engineers. 
•    Capable of self-directed project delivery and can help the team to prioritize work that maximizes team impact, including avoiding getting stuck on unimportant details. 
•    Participates in the definition and delivery of work or projects within the team. 
•    Escalates matters when appropriate without getting into rabbit holes or blocked for long time periods. Exercises a good sense of timeboxing tasks and able to proactively address issues before they become problematic

•    Proficiency in SQL
•    Proficiency with data transformation tools (e.g. dbt) 
•    Proficiency with data modeling approaches and philosophies (e.g. Kimball, OBT)  
•    Proficiency with business intelligence platforms (e.g. Sigma, PowerBI) 
•    Proficiency with at least one cloud data warehouse (e.g. Snowflake) 
•    Proficiency with version control and git 
•    Knowledge of other common programming languages for data manipulation (e.g. Python, R)
•    Knowledge of common data integration patterns (e.g. CDC, ELT, etc.) 
•    Knowledge of common data integration / orchestration platforms (e.g. Fivetran, Azure Data Factory, Apache Airflow) 

Bonus points for: 
•    Data Analysis: You have a basic understanding of data analysis and can identify deviations from the norm and relationships between various data points.  
•    Data Movement: You can import large, assorted datasets from multiple sources into a single storage medium, wherein you understand event-driven architectures and asynchronous pub/sub design patterns

• Frequent sitting at a desk performing work on a computer
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions

Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role.