Job VC
Data / Analytics Engineer
Technologies
Description
We are looking for a Data / Analytics Engineer to join an ongoing initiative focused on building a scalable, AI-driven data platform for product analytics.
This is a new role within an existing analytics team, where you’ll help architect and build the core data layer that will enable self-service analytics across the company. The role combines hands-on data engineering with forward-looking AI automation initiatives.
Client/Project:
is a global DevOps company.
The project focuses on building a Gold data layer that enables product and business stakeholders to access ready-to-use, reliable datasets without relying on ad-hoc requests.
It’s a complex and high-impact domain, where:
you will design and build a scalable data model from multiple sources (Snowplow, FullStory, internal systems)
the team is transitioning to an AI-first development approach (Cloud Code, Codeium — no manual SQL writing mindset)
AI agents are a core part of the vision (automating data transformations and schema evolution)
the work has a direct impact on decision-making across a ~600-person product organization
Stage:
existing (early stage of data platform transformation — Gold layer not yet built)
Position:
new
Timezone requirements:
CET ±2h preferred
Location requirements:
Remote
Client team:
Analytics Guild (up to 9 people)
English:
Upper-Intermediate+
Requirements:
4+ years of experience in Data Engineering / Analytics Engineering (Mid+ or Senior level)
Strong
SQL
skills (advanced: window functions, CTEs, joins, performance tuning)
Hands-on experience with
dbt
(models, testing, documentation)
Experience with
Amazon Redshift
(or strong AWS DWH background)
Solid understanding of
data modeling
(dimensional modeling, medallion architecture)
Experience with
Python
for data pipelines, integrations, or data quality
Practical experience using
AI coding tools
(Copilot, Codeium, Claude, etc.) in daily work
Experience working with product/event data
Strong ownership and ability to work autonomously
Clear communication skills and the ability to work with non-technical stakeholders
Nice to have:
Experience building or experimenting with
AI agents
(LangChain, OpenAI API, etc.)
Familiarity with
Snowplow
or similar event tracking tools
Familiarity with
FullStory
or behavioral analytics tools
Experience with AWS ecosystem (S3, IAM, etc.)
Experience with orchestration tools (e.g., Airflow)
Understanding of BI tools (e.g., Looker) and data consumption patterns
Responsibilities:
This role evolves in two main phases:
Phase 1 (Months
1-4):
Building the Gold Layer
Design and implement
Gold-layer data models
in dbt
Transform raw and Silver-layer data into business-ready datasets
Integrate multiple data sources (Snowplow, FullStory, internal systems)
Collaborate with the analytics team to translate business needs into reusable models
Identify and fix inconsistencies in existing data layers
Establish
data quality, monitoring, and observability practices
Phase 2 (Months
4-6+):
Automation & AI
Design and build
AI agents
for automating data workflows
Develop systems that detect new incoming events and suggest schema placement
Automate maintenance of the Gold layer
Explore and implement AI-driven approaches for data transformations
Ownership areas:
Gold data layer architecture
Data quality and reliability
AI-driven automation of data workflows
Collaboration with analytics stakeholders
Long-term scalability of the data platform
Ideal candidate:
Has an
AI-first mindset
and is excited about automation and agents
Thinks like a
data architect
, not just a query writer
Comfortable working in a
fast-paced, direct communication culture
Self-driven and able to operate with minimal supervision
Able to translate business questions into scalable data models
Interested in building systems, not just solving one-off tasks
Comfortable working in a remote, cross-cultural team
Benefits from 8allocate:
Team & Culture: Team events, offsites, and a culture that keeps people connected.
Learning & Development: Budget for courses, certifications, and conferences.
Wellbeing: Flexible support in line with company policy, with options to support your physical and mental wellbeing (sport, mental health, or medical insurance).
Rest & Recovery: Paid vacation and sick leave.
This is a new role within an existing analytics team, where you’ll help architect and build the core data layer that will enable self-service analytics across the company. The role combines hands-on data engineering with forward-looking AI automation initiatives.
Client/Project:
is a global DevOps company.
The project focuses on building a Gold data layer that enables product and business stakeholders to access ready-to-use, reliable datasets without relying on ad-hoc requests.
It’s a complex and high-impact domain, where:
you will design and build a scalable data model from multiple sources (Snowplow, FullStory, internal systems)
the team is transitioning to an AI-first development approach (Cloud Code, Codeium — no manual SQL writing mindset)
AI agents are a core part of the vision (automating data transformations and schema evolution)
the work has a direct impact on decision-making across a ~600-person product organization
Stage:
existing (early stage of data platform transformation — Gold layer not yet built)
Position:
new
Timezone requirements:
CET ±2h preferred
Location requirements:
Remote
Client team:
Analytics Guild (up to 9 people)
English:
Upper-Intermediate+
Requirements:
4+ years of experience in Data Engineering / Analytics Engineering (Mid+ or Senior level)
Strong
SQL
skills (advanced: window functions, CTEs, joins, performance tuning)
Hands-on experience with
dbt
(models, testing, documentation)
Experience with
Amazon Redshift
(or strong AWS DWH background)
Solid understanding of
data modeling
(dimensional modeling, medallion architecture)
Experience with
Python
for data pipelines, integrations, or data quality
Practical experience using
AI coding tools
(Copilot, Codeium, Claude, etc.) in daily work
Experience working with product/event data
Strong ownership and ability to work autonomously
Clear communication skills and the ability to work with non-technical stakeholders
Nice to have:
Experience building or experimenting with
AI agents
(LangChain, OpenAI API, etc.)
Familiarity with
Snowplow
or similar event tracking tools
Familiarity with
FullStory
or behavioral analytics tools
Experience with AWS ecosystem (S3, IAM, etc.)
Experience with orchestration tools (e.g., Airflow)
Understanding of BI tools (e.g., Looker) and data consumption patterns
Responsibilities:
This role evolves in two main phases:
Phase 1 (Months
1-4):
Building the Gold Layer
Design and implement
Gold-layer data models
in dbt
Transform raw and Silver-layer data into business-ready datasets
Integrate multiple data sources (Snowplow, FullStory, internal systems)
Collaborate with the analytics team to translate business needs into reusable models
Identify and fix inconsistencies in existing data layers
Establish
data quality, monitoring, and observability practices
Phase 2 (Months
4-6+):
Automation & AI
Design and build
AI agents
for automating data workflows
Develop systems that detect new incoming events and suggest schema placement
Automate maintenance of the Gold layer
Explore and implement AI-driven approaches for data transformations
Ownership areas:
Gold data layer architecture
Data quality and reliability
AI-driven automation of data workflows
Collaboration with analytics stakeholders
Long-term scalability of the data platform
Ideal candidate:
Has an
AI-first mindset
and is excited about automation and agents
Thinks like a
data architect
, not just a query writer
Comfortable working in a
fast-paced, direct communication culture
Self-driven and able to operate with minimal supervision
Able to translate business questions into scalable data models
Interested in building systems, not just solving one-off tasks
Comfortable working in a remote, cross-cultural team
Benefits from 8allocate:
Team & Culture: Team events, offsites, and a culture that keeps people connected.
Learning & Development: Budget for courses, certifications, and conferences.
Wellbeing: Flexible support in line with company policy, with options to support your physical and mental wellbeing (sport, mental health, or medical insurance).
Rest & Recovery: Paid vacation and sick leave.