Time since we first saw it
Day 6
Early. Similar jobs usually come down between day 7 and day 53.
From 7,797 similar postings that came down. Coming down is not always a hire.
Reads like a Data Leadership role: running a data team.
Employment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Lead and coach analytics engineers and web analytics specialists, setting priorities
Design and evolve SQL-based models and metric definitions in the semantic layer
Turn ad hoc requests into reusable, tested, documented datasets with automated data-quality checks
Set modeling, version control, testing and code review standards across the data organization
Lead and grow the team. Set priorities, coach, and keep a steady delivery rhythm across analytics engineering and web analytics.
Design, build and evolve SQL-based models and metric definitions so that one number means one thing across all our products.
Move the team from ad hoc requests to reusable, tested, documented datasets that people and our AI data assistant can query directly, with tracking checks and data-quality monitoring on autopilot.
Modeling conventions, version control, testing and release practices, code review
Experience. 5+ years in analytics, data or BI engineering, including 2+ years leading a team or leading others technically.
Data modeling and pipelines. You design warehouse and mart models that several teams build on, including a semantic layer, and choose the methodology (star schema, Data Vault or equivalent) to fit the queries it must serve. You optimize and troubleshoot pipelines on a cloud stack such as AWS (Athena, Redshift, Glue) and orchestration tools such as Airflow.
Leadership and ownership. You own complex team-level projects, make high-impact decisions from complex information, coach people by spotting skill gaps and giving regular feedback, set clear scope expectations, and turn change into actionable steps for the team.
From the employer’s posting.
airSlate is a global SaaS technology company that develops no-code workflow automation, electronic signature, and document management solutions.
In the employer’s words · Read in context
Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 6
Early. Similar jobs usually come down between day 7 and day 53.
From 7,797 similar postings that came down. Coming down is not always a hire.
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Experience. 5+ years in analytics, data or BI engineering, including 2+ years leading a team or leading others technically. - Expert SQL and Python. You set query and performance standards others follow, rewrite expensive queries and explain the trade-offs, handle incremental loads, deduplication and slowly changing dimensions, and own production Python components that other teams depend on. - Data modeling and pipelines. You design warehouse and mart models that several teams build on, including a semantic layer, and choose the methodology (star schema, Data Vault or equivalent) to fit the queries it must serve. You optimize and troubleshoot pipelines on a cloud stack such as AWS (Athena, Redshift, Glue) and orchestration tools such as Airflow.
- Lead and grow the team. Set priorities, coach, and keep a steady delivery rhythm across analytics engineering and web analytics. - Own the semantic layer. Design, build and evolve SQL-based models and metric definitions so that one number means one thing across all our products. - Raise data curation and automate the routine. Move the team from ad hoc requests to reusable, tested, documented datasets that people and our AI data assistant can query directly, with tracking checks and data-quality monitoring on autopilot.
- Expert SQL and Python. You set query and performance standards others follow, rewrite expensive queries and explain the trade-offs, handle incremental loads, deduplication and slowly changing dimensions, and own production Python components that other teams depend on. - Data modeling and pipelines. You design warehouse and mart models that several teams build on, including a semantic layer, and choose the methodology (star schema, Data Vault or equivalent) to fit the queries it must serve. You optimize and troubleshoot pipelines on a cloud stack such as AWS (Athena, Redshift, Glue) and orchestration tools such as Airflow. - Engineering discipline. You set branching and code review standards, require schema and data tests and CI before release, and lead risky changes with a clear rollout and rollback plan.
- Communication. You clarify complex ideas, build consensus across teams and time zones, and communicate in fluent English. - Nice to have: experience with dbt or a similar transformation framework (we may adopt one); web analytics and tracking (GTM, GA4); B2B SaaS or document-workflow / e-signature background. What we offer
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Already applied? Track this application
We are looking for a hands-on leader to run Data Enablement. The team that owns how our data is modeled, trusted, and made ready for the business and for AI. You will lead analytics engineers and web analytics specialists, own the semantic layer end-to-end, and turn raw product and event data into curated, well-documented datasets that people can rely on.
Own the semantic layer. Design, build and evolve SQL-based models and metric definitions so that one number means one thing across all our products.
Raise data curation and automate the routine. Move the team from ad hoc requests to reusable, tested, documented datasets that people and our AI data assistant can query directly, with tracking checks and data-quality monitoring on autopilot.
Set standards and partner across the data organization. Modeling conventions, version control, testing and release practices, code review; work with Data Business Partners, Data Science / ML and Data Platform on scope and ownership. Stay close to the code on critical builds.
Full-time
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Poland
What we offer Flexible working environment - Our teams operate across the globe. We value in‑person collaboration in our hubs, but we also embrace remote and hybrid working. You can work from one of our offices in the United States, Poland, Romania or Ukraine, or remotely from many countries. Competitive compensation and stock options - We offer salaries that reflect local market conditions and experience, plus a performance-based bonus system and stock options so you share in the company’s growth.
This passage needs a closer read in the full description.