Senior Analytics Engineer

SharkNinja

4d•$116,300 - $155,000•Remote

About The Position

This role owns data products end to end: onboarding a new source, orchestrating it, modeling it, and delivering something a business team can act on. You'll need strong SQL and hands-on Python coding experience, along with solid working knowledge of Snowflake, dbt Cloud, and an orchestrator like Dagster or Airflow. We work in an AI-assisted environment and expect this role to use AI coding tools daily, without lowering the bar on correctness or governance.

Requirements

Expert SQL and deep familiarity with Snowflake
Real depth in ETL/ELT and data modeling, with dbt Cloud experience
Hands-on with Dagster or Airflow, including partitioning and backfills
Pipelines you designed that kept working as volume grew
Third-party APIs and file feeds pulled into a warehouse, plus the auth and secret management around them
Owned pipeline reliability in production: alerting, triage, and follow-through after an incident
Set or raised engineering standards on a team, in writing, and made them stick
Used AI coding assistants in a real production workflow, with judgment about where they help and where they cause trouble.
Clear communication with non-engineers, vendors and stakeholders
Bachelor’s degree in Computer Science, Data Science, Engineering, or related field, or equivalent practical experience.
3-5 years of experience in data engineering and analytics

Nice To Haves

Having written the guardrails for a team is a plus
Advanced degrees or professional certifications related to data engineering are preferred

Responsibilities

Onboard new sources end to end: vendor REST APIs, SFTP and S3 file drops, on-prem file shares
Handle pagination, rate limits, retries, and incremental extracts
Design connection auth: key-pair, token rotation, environment-variable contracts
Actively work with vendors and source owners on access, file cadence, and schema changes
Lead orchestration pipeline orchestration: schedules, backfills, and various checks and tests
Account for partitioned incremental loads, deliberate concurrency
Build idempotent loads with an explicit merge grain, so a failed run is fixed by re-running it
Own alerting, triage, and root cause on failed runs
Keep warehouse sizing and query cost in check
Write Python tests, dbt tests, keep CI green
Set the standards: module structure, config and secrets, and what a pipeline needs before it ships
Write them down and enforce them in code review
Build and maintain dbt Cloud models on Snowflake, from staging through marts, with tests and docs, following repo standards
Write reusable dbt macros with Jinja to keep models DRY and consistent
Use the available AI tools for dbt models, SQL refactoring, scaffolding, and docs
Review and test everything they produce
Document how the team should use these tools, and what not to hand them
Git and code review, mentoring junior engineers, and following our dependency and data governance policies