Senior Analytics Engineer

About Us

Shark Ninjais a global product design and technology company, with a diversified portfolio of 5-star rated lifestyle solutions that positivelyimpactpeople’s lives in homes around the world.
Powered by two trusted, global brands, Shark and Ninja
, the company has a proven track recordof bringing disruptive innovation to market and developing one consumer product after another has allowed

Shark

Ninjato enter multiple product categories, driving significant growth and market share gains. Headquartered in Needham, Massachusetts with more than4,100associates, the company’s products are sold at key retailers, online and offline, and through distributors around the world.

At Shark Ninja, we’re building an AI-native culture. We’re not waiting for the future; we’re creating it. Our people are expected to experiment boldly, adopt new tools, and continuously raise what’s possible to create meaningful impact for our consumers. If you believe the best way to do your job hasn’t been invented yet, you’ll fit right in.

Location:
Needham, MA or Remote US

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.

• 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

• 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. Having written the guardrails for a team is a plus
• Clear communication with non‑engineers, vendors and stakeholders

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field, or equivalent practical experience.
• Advanced degrees or professional certifications related to data engineering are preferred
• 3-5 years of experience in data engineering and analytics

T…