> Markdown version of [/jobs/ext/2730775-senior-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/2730775-senior-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - **Company:** Sharkninja Dna. - **Location:** Needham, MA, United States (Remote available) - **Experience:** Expert - **Salary:** $116,300.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon S3, Cloud Computing, Code Review, Information Engineering, Extract Transform Load (ETL), Jinja (Template Engine), Python (Programming Language), Standard Sql, SQL Databases, File Transfer Protocol (FTP), Snowflake, Git, Information Technology, Restful APIs, Web Api - **Published:** September 5, 2026 - **Apply:** https://www.dice.com/job-detail/769f5309-9ddc-4fda-aa78-5775c814cb28 ## About the Role * 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 ## Description 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. What you'll do Onboarding new data sources * 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 Reliable, scalable pipelines * 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 Testing and standards * 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 Modeling and analytics * 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 AI-assisted development * 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 Team practices * Git and code review, mentoring junior engineers, and following our dependency and data governance policies What we're looking for * 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Web APIs you might not know about](https://www.wearedevelopers.com/videos/281-web-apis-you-might-not-know-about) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)