Sr. Analytics Engineer

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Senior Analytics Engineer - Marketing Data

Conversion expectation discussed $175K/yrs. + range

Work Authorization Client does not provide sponsorship

Interview Process 1st- Manager

2nd - Team

Potentially a 3rd with Dir.

Team Overview Marketing:

Project Description We are looking for a Senior Analytics Engineer to design and build the data structures that support our customer data platform (CDP) and broader marketing data needs. This is a hands-on technical role at the intersection of data engineering, data modeling, business analysis, and marketing technology. The person in this role will work directly with marketing and technical stakeholders to understand business use cases, determine what data is available from upstream platforms, and translate those findings into actionable requirements and reliable Snowflake data solutions.

Initially, this role will focus primarily on our CDP implementation. The Senior Analytics Engineer will identify and build the models and materializations needed to support CDP use cases, audience activation, customer insights, and related downstream processes. Over time, the role will expand to support the broader data needs of our Marketing Analytics team. This is not primarily a dashboard or visualization development role. Success requires the ability to understand complex marketing data, impose structure on ambiguous or rapidly evolving requirements, and build fit-for-purpose datasets for downstream consumers.

• The role is more focused on data preparation and data quality upstream of the CDP.

• Candidate needs to develop a strong working understanding of the data, what needs to feed the CDP, and the requirements/use cases that data needs to support.

• Looking for someone with the technical judgment to understand whether the available data can actually support the requested business use case.

Duties/Day to Day Overview • Partner with business and technical stakeholders to understand CDP use cases and determine whether available data is sufficiently detailed, complete, timely, and appropriately structured to support them:

• Identify, design, and build the Snowflake models, aggregations, materializations, and other data structures needed to support CDP performance and usability, using views, dynamic tables, semantic views, stored procedures, and other appropriate technologies.

• Support the continued evolution of CDP and marketing data requirements following initial implementation.

• Develop a strong working understanding of the data produced by marketing and ecommerce platforms, including web behavioral events, campaign interactions, customer identifiers, orders, and related business activity.

• Elicit the underlying business need from requests that may initially be incomplete, unstructured, or solution-oriented; identify gaps and contradictions and distinguish among ingestion, modeling, CDP, activation, and reporting requirements.

• Challenge assumptions or proposed solutions when they do not align with the available data, system capabilities, or intended business outcome.

• Analyze source-system data and API documentation to determine whether the necessary objects, fields, identifiers, events, and levels of detail are available.

• Translate business needs and source-system findings into clear, technically actionable Jira tickets and acceptance criteria for data ingestion and modeling work.

• Communicate technical constraints, tradeoffs, data limitations, and scope boundaries clearly as stakeholder requests and project priorities evolve.

• Write advanced, performant SQL to transform, combine, and prepare data in Snowflake.

• Develop Snowflake stored procedures and supporting utilities using Python, JavaScript, or Snowflake Scripting, with Python as the preferred language.

• Evaluate generated or externally supplied code for correctness, maintainability, performance, security, and alignment with engineering standards.

• Work within established development practices, including Git, version control, peer review, automated testing, and CI/CD.

• Partner with data engineers to define ingestion requirements and map business needs to the objects, endpoints, fields, identifiers, and extraction methods available from marketing, ecommerce, and customer-data sources.

• Build or modify data ingestion processes when needed, while generally partnering with other data engineers for primary pipeline development.

• Troubleshoot source-data and integration issues in collaboration with internal engineers and external vendors.

• Validate modeled data against source systems, business expectations, and downstream use cases, including identifier coverage, record completeness, event granularity, and business-rule accuracy.

• Perform user acceptance testing on upstream datasets delivered by other engineers and identify missing, incorrect, or unusable data before downstream implementation.

• Participate in troubleshooting data incidents and implement durable fixes for issues involving owned models or processes.

Top Requirements:

(Must haves)

• Approximately five or more years of relevant professional experience in analytics engineering, data engineering, marketing data, or a closely related role.

• Professional, hands-on experience working with Google Analytics data in a business or production environment. Coursework, certifications, or self-directed learning without applied work experience are not sufficient.

• Professional experience supporting data in a retail or ecommerce environment.

• Advanced SQL skills and hands-on experience designing fit-for-purpose data models, transformations, and materializations in Snowflake.

• Working proficiency in Python, including the ability to read, evaluate, troubleshoot, and improve code written by others.

• Experience reviewing API documentation and mapping API objects, endpoints, fields, and identifiers to business requirements.

• Experience with Git and version-controlled development practices, including code review, testing, and CI/CD.

• Demonstrated ability to elicit requirements from stakeholders, bring structure to ambiguous or nonlinear discussions, and translate business needs into precise, actionable technical work.

• Strong understanding of data validation across multiple stages of a data pipeline.

• Exceptional written and verbal communication skills in English.