Podcast
Master Analytics Engineering: Skills, Toolstack, Career Roadmap
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Master Analytics Engineering: Skills, Toolstack, Career Roadmap
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Episode Overview
How do you become an effective analytics engineer and what skills, tools, and career steps matter most? In this episode, Victoria Perez Mola—born in Argentina, trained as a Systems Engineer and now an Analytics Engineer at Tier in Berlin—walks us through her move from ERP and finance reporting into analytics engineering. We cover daily responsibilities like data modeling, pipelines, data quality and Looker; the DBT workflow (SQL transformations, version control, tests, DAG); and a practical analytics toolstack.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:48 - Guest Introduction: Victoria Perez Mola overview
- 2:45 - Career Journey: Systems engineering, ERP & finance reporting
- 4:05 - Daily Responsibilities: Data modeling, pipelines, data quality, Looker
- 6:49 - DBT Overview: SQL transformations, version control, tests, DAG
- 10:04 - Analytics Toolstack: DBT, Snowflake, Adlib ETL, Looker
- 11:48 - Transition Story: From BI/ERP work to analytics engineering
- 14:34 - Role Comparison: Analytics Engineer vs Data Analyst vs Data Engineer
- 16:54 - Role Origins & Purpose: Spotify, reducing analysts’’ cleaning workload
- 20:52 - Job Expectations: Example posting traits (pipelines, auditing, dashboards)
- 26:10 - Core Skills: SQL, dimensional modeling, Snowflake and tooling variance
- 30:06 - DBT Ecosystem: DBT’‘s role in the analytics engineer movement
- 31:09 - Organizational Variability: Team setups and role definitions across companies
- 33:02 - Cross-functional Collaboration: Working with analysts, data scientists, backend
- 36:44 - Managing Bad Data & Schema Changes: DBT cleaning, macros, limitations
- 38:53 - Data Testing Strategy: DBT tests, upstream checks, warnings vs errors
- 40:42 - BI Roles vs Analytics Engineering: Overlaps with BI developer and analyst
- 42:05 - Pathway to Analytics Engineering: Software practices, Kimball, DBT learning
- 43:39 - Learning Resources: DBT tutorials and ‘‘Analytics readings’’ Notion list
- 44:52 - Role Fit Signals: Enjoy modeling, quality, and best practices
- 46:28 - Job Frustrations: Enforcing guidelines, ad-hoc firefights, limited raw control
- 48:36 - Team Scale & Placement: Platform teams vs embedded analytics engineers
- 50:46 - Data Documentation & Profiling: DBT docs strengths and profiling tools (Datafold,