Analytics Engineering Team Lead
New
A
airSlateSaaS workflow automation
Workplace type: Remote; Listing location: PolandFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- Communicate in fluent English
- Experience
- 5+ years in analytics, data or BI engineering, including 2+ years leading a team or leading others technically.
- Required Skills
- AWSPythonSQLAirflowData modelingdbt
Requirements
- Bring 5+ years of experience in analytics, data, or BI engineering.
- Have 2+ years leading a team or leading others technically.
- Demonstrate expert SQL skills, including query performance, incremental loads, deduplication, and slowly changing dimensions.
- Have expert Python skills and experience owning production Python components.
- Design warehouse and mart models, including a semantic layer, using star schema, Data Vault, or an equivalent methodology.
- Optimize and troubleshoot pipelines on a cloud stack such as AWS (Athena, Redshift, Glue) and orchestration tools such as Airflow.
- Establish branching, code review, schema and data testing, and CI practices, with rollout and rollback plans for risky changes.
- Design data validation, investigate unfamiliar sources, address root causes, and set documentation standards.
- Design solutions for complex problems, define cross-team work and completion criteria, and build decision-focused dashboards.
- Use AI tools in team workflows and evaluate AI outputs for quality, privacy, and bias.
- Coach team members, identify skill gaps, give regular feedback, and set clear scope expectations.
- Communicate 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, web analytics and tracking (GTM, GA4), or B2B SaaS or document-workflow / e-signature background.
Responsibilities
- Lead, coach, and set priorities for analytics engineering and web analytics specialists.
- Design, build, and evolve SQL-based models and metric definitions for the semantic layer.
- Turn raw product and event data into reusable, tested, and documented datasets.
- Automate tracking checks and data-quality monitoring.
- Set modeling, version control, testing, release, and code review standards.
- Partner with Data Business Partners, Data Science / ML, and Data Platform on scope and ownership.
- Stay hands-on with critical builds.
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