Senior Analytics Engineer

Location: Needham, MA or Remote US

Role summary

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.

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

Education and experience:

• 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