Senior Product Data Engineer; remote, Europe

Remote — Data Insights Team — Full-time

Modash gives brands the tools to work with the right content creators and helps creators earn a living doing what they love. Behind the scenes, the Data Insights team is building the intelligence layer that turns raw social media signals into trusted, customer-facing data products — with reliable access, quality, and freshness at scale.

We’re looking for a hardened Senior Product Data Engineer to help us scale these systems end-to-end, raise our quality bar, and accelerate how quickly we turn messy public data into consistent, valuable insights customers can build on.

We’re not a service function — Data Search & Data Insights are core product capabilities at Modash, building products for customers to use.
Data Insights is a specialised team in Data Org, and you’ll own high impact projects end-to-end, from idea to launch.

Here’s a typical day:

• Start your day with a short standup
• Heads-down focus time to plan, build, iterate, and launch
• Minimal meetings — maximum ownership

You’ll be working on big, impactful projects like:

• Creating an understanding of the creators location, age, and interests at scale
• Creating systems to extract collaborations between creators and brands from raw social data
• Shaping the future of AI-assisted search, exploring how LLMs and embeddings can enhance search and recommendations

You won’t be patching pipelines — you’ll be creating data products from scratch that directly impact customers.

At Modash, the Data Insights team isn’t a support function — it’s a core part of the product. You’ll join a growing group of data and backend engineers, working within our broader Data organization.

We work in three closely aligned teams within Data:

• Data Insights — builds the creator and brand-level insight products and APIs (e.g., collaborations, reports, dictionaries, contacts, audience overlap).
• Data Search — owns our search products (including AI Search) end-to-end.
• Data Core — responsible for raw data collection and the foundations of our data platform.

We value autonomy, but we also work closely as a team — through pair programming, fast feedback loops, and shared wins. Everyone is expected to take ownership, but nobody works in isolation.

We’re remote-first, and we also make time to connect IRL through regular team offsites — to have fun, collaborate, and reflect.

• AWS and GCP with Pulumi (IaC)
• PySpark on AWS EMR for compute
• GCP Vertex Batch API for LLMs
• Airflow for orchestration
• Iceberg and Aurora (Postgres) for persistence
• Other: S3, Glue, Kinesis, Lambda, ECS, Athena
• Tools:
  Slack, Git Hub, Linear, Notion, Cursor

We move fast. You can get interviewed in under a week. Process consists of:

• Strong knowledge of Spark (Scala, Databricks, or PySpark; PySpark preferred but not required)
• Proven track record with ETL/ELT pipelines and large-scale data processing
• Comfortable working with unstructured data
• Experience with workflow orchestration tools like Airflow or AWS Step Functions
• Familiarity with the AWS ecosystem (Glue, EMR, etc.)
• You've shipped full features from idea to production: planning & scoping → architecture → implementation → release → iteration
• Based in Europe with significant working-hours overlap with EET (Tallinn time)
• Hands-on experience building agentic / LLM-powered features in production
• Practical understanding of trade-offs between LLMs (cost, latency, capability)

• Have worked with AI/ML tools or LLMs
• Are familiar with the GCP stack (especially Vertex AI)
• Have worked with lakehouse formats like Apache Iceberg
• Have used Pulumi or Terraform for IaC
• Are familiar with Node.js and Type Script
• Understand AWS cost mechanics (how scale impacts spend)
• Care deeply about code quality and system design
• Are curious about the creator economy

• Your data engineering experience is primarily in analytics or BI (dashboards, internal reporting, warehouse modeling for analysts)
• You haven’t built…