Senior Data Engineer (fmd)

We maximize product availability with minimal cashflow investment in 1/10 of the time. We solve a real problem for SMEs. With AI.

The problem we solve: Mid-size Shopify brands lose revenue and cash to stockouts and inefficiencies every day. They can see the problem. They cant fix it fast enough.

What VOIDS does: VOIDS is the AI brain for mid-size Shopify brands. We forecast demand at the product level catch stockouts and inefficiencies before they happen and tell e-commerce teams exactly what to do or execute it automatically with a click.

The result: 98% inventory efficiency. 20x ROI. Six-figure cash unlocked. Within weeks.

Traction: Launched June 2023. Since then: 300% growth 1B data points processed 2M ARR 50 brands live including Hyrox 6pm Creamyfabrics and NatureHeart. Now targeting 10M ARR by 2027.

Where were going: Today we own demand forecasting and stock management. Tomorrow: fully autonomous AI-driven procurement. Were not building features were rebuilding how modern commerce operates.

Why join now: Were a small fast team where every hire shapes the companys trajectory. Youll work directly with Jannik and Tobias - two founders who live and breathe e-commerce and AI - and own how we ingest process and activate 1B data points across our platform. This isnt a maintenance role. Youll build the data foundation for a fully AI-driven future.

With high autonomy. At real data scale. With real impact.

Tasks

As a Senior Data Engineer youll own the backbone of our product: scalable robust data pipelines that power our web app machine learning models and optimization algorithms. Your role is pivotalevery aspect of VOIDS depends on clean organized and reliable data.

Our data pipelines already handle over in yearly revenue flowing through customer transactions. Your mission is to build and continuously refine efficient data workflows that seamlessly support complex analytics accurate forecasting and agile product development. Youll thrive in complexity transforming massive transactional datasets into actionable insights for diverse e-commerce brands. Specifically you will:

• Efficiently source process and structure diverse datasetssuch as transactional behavioral product and marketing datainto clean actionable formats for our AI models optimization algorithms and software engineering teams.
• Ensure data reliability cleanliness and timeliness proactively identifying and addressing bottlenecks or inconsistencies.
• Deeply understand the product customer problems and data specifics to proactively identify anticipate and resolve data-related issues.
• Act as the first point of action for new data needs rapidly delivering solutions that enable the rest of the team to iterate fast and independently.
• Collaborate closely with the CTO data scientists software engineers and customer success teams to translate business requirements into robust data solutions.
• Continuously improve our data infrastructure optimize workflows and advocate for best practices across the engineering and data science teams.
• Actually get things done deciding yourself what to focus onwithout bureaucracy.

Requirements

Must-Have Skills

• Fluent English (German is a plus)
• Clear professional asynchronous communication abilities
• 3 years of experience in Data Engineering or related roles
• Proven 3 years experience in Python particularly with data manipulation libraries (Pandas Polars) for efficient data processing
• Strong proficiency in reading writing and updating data in both structured (SQL databases especially PostgreSQL) and unstructured (AWS S3 Parquet) storage solutions
• Hands-on experience building and maintaining scalable batch data pipelines and workflows as inputs for web applications and AI models (AWS Lambda Airflow MLflow AWS SageMaker)
• Proven ability to set up and maintain robust testing environments and manage efficient DataOps/MLOps workflows to enable rapid iteration
• Familiarity with infrastructure and containerization frameworks (Kubernetes Docker Terraform)
• Solid expertise in data storage solutions like AWS S3 (Parquet)
• Ability to design and implement clean reliable and efficient data processing pipelines and APIs
• Strong product intuition and understanding with a proactive ownership-oriented mindset
• Comfort with ambiguity and autonomy in problem-solving
• Daily use of AI tools to enhance productivity development speed and problem-solving.

Bonus / Nice-to-Have

• Experience in B2B SaaS startups / scaleups
• Experience with eCommerce data sets and solutions (Shopify Amazon Seller Central Google Ads Meta Ads Klaviyo Channable etc.)
• Familiarity with scalable big data tools and frameworks (dbt dask Apache Spark EMR Databricks AWS Glue)
• Familiarity or interest in Data Science workflows especially related to time series forecasting (Nixtla Darts statsmodels sktime)
• Experience with streaming data pipelines
• Contributions to developer experience data observability or internal tooling improvements

Tech Stack

• Programming: Python (Pandas Polars) SQL
• Processing: AWS SageMaker AWS Lambda
• Data Storage & Management: PostgreSQL AWS S3 (Parquet) BigQuery
• ML Infrastructure: AWS SageMaker AWS Lambda MLflow
• Orchestration: Airflow on AWS
• Collaboration & AI Tools: GitHub Copilot ChatGPT
• Containerization: Kubernetes (Airbyte hosting for data sourcing)

Optional if youre interested in expanding into data science tasks (full-stack mindset appreciated):

• Modeling & Analytics: Statistical ML and neural time series forecasting (Nixtla statsmodels XGBoost)

Benefits

What Youll Get

• Permanent full-time contract (no B2B)
• Competitive salary () Equity
• 30 days paid vacation
• All AI subscriptions with unlimited usage you want
• New Mac Book Pro & min. 2 Monitors in the office ;)
• Regular team events and quarterly off-sites
• Real ownership and influence
• A calm focused work environment that rewards initiative
• Wellpass membership to unlimited fitness yoga swimming climbing and more

We care less about titles and more about impact.

When you apply tell us:

• A broken thing you fixed technically or organizationally
• A feature you shipped that delighted users and its financial impact for your company
• An internal tool or DX improvement you pushed
• What motivates you and the kinds of product problems you enjoy