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Senior Analytics Engineer

Velir LogoVelir
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Salary

$130,000 - $150,000

Location

Remote

Senior 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.

We are only accepting candidates that reside in the US and do not require any current or future visa sponsorship.

Responsibilities

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. As a senior member of the Analytics Engineering function, you serve as a mentor to other engineers, both individually and in group settings.

  • Designs data models, implements data governance, selects and implements transformation and analytics tools, and creates efficient data querying and processing methods.
  • Configures and optimizes aspects of a cloud data warehouse such as data permissions, compute and storage clusters, and table schemas.
  • Works with tools like business intelligence (BI) platforms and data visualization tools.
  • Focuses on opportunities to reduce complexity within client data stacks and delivering added value for downstream use cases.
  • Provides constructive feedback on peer code or solution design document reviews.
  • Serves as a mentor for less advanced team members and onboarding new engineers onto the team, helping evolve and document the standards for the analytics engineering practice at Velir.

Cross-Team Collaboration: You are responsible for collaborating with peers and other functional departments to develop and implement analytics engineering strategies and approaches that support engagement goals and understanding client needs.

  • Promotes a positive culture within and across different teams, collaborating with data engineers and data analysts on end-to-end client requirements.
  • Collaborates with clients and functional managers to plan for analytics engineering needs for a product or feature launch.
  • Pairs with a teammate or with someone at a client on strategies for solving an analytics engineering problem.
  • Creates a process or reporting template that helps cross-functional teams solve for common analytics engineering problems.
  • Regularly engages with other teams to make our organization more effective.
  • Takes initiative to identify and solve important problems. Coordinates with others on cross-cutting technical issues.
  • Drives data solutions improvements that impact the client experience or empowers internal stakeholders (teams like Operations, Customer Support, Finance, etc.) to do their job effectively.

Project Delivery: You are responsible for ensuring that large and/or more complex analytics engineering projects are delivered on time, within scope, and within budget.

  • Optimizes for the predictability and regular cadence of deliverables.
  • Keeps reliability, maintainability and scalability of our clients’ systems top of mind.
  • Embraces long-term ownership of projects while training others to reduce the bus factor or becoming a blocker.
  • Prioritizes and values undesirable/unowned work that enables the team to move faster.

Skills&Qualifications

  • Expert in SQL
  • Expert with data transformation tools (e.g. dbt)
  • Expert with at least one cloud data warehouse (e.g. Snowflake)
  • Expert with version control and git
  • Proficiency with data modeling approaches and philosophies (e.g. Kimball, OBT)
  • Proficiency with business intelligence platforms (e.g. Sigma, PowerBI)
  • 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 thorough 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.

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