Data Engineer

• We are building an internal superagent: an AI layer over all of Synera’s business data and tools that every employee can delegate work to
• Its intelligence is only as good as the data underneath it, and that data foundation is yours to own, build, and shape
• That means the data warehouse, expanded from a GTM reporting store into Synera’s system of insight: GTM data, product telemetry, engineering work, support, finance, and everything in between
• It means every connector into it and the interfaces that let agents query it
• And it means working with every team to get their data out of heads and meetings and into a form an agent can use
• An agent is only as intelligent as the data underneath it
• We are looking for a builder
• You will own a whole machine, not a slice of one, with the mandate, the modern stack, and the AI tooling to build leverage instead of backlog. You will lay the data foundation for Synera’s superagent, and you report into Business Operations with a direct line to how the company is run
• Own the data warehouse (Timescale): Synera’s single source of truth for data, it’s design and development to always be cutting edge
• Extract data from our GTM and business systems (Salesforce, HubSpot, product, finance, …) into our data warehouse reliably, and structure & annotate it so it can be queried
• Build and own data flows: ETL pipelines (Airflow/Timescale), data models, and the sync jobs that keep systems consistent
• Turn data into foresight: build the reporting and forecasting layer (Power BI, Salesforce reports, forecast models) that leadership runs on and extrapolate from pipeline, funnel, and usage data to where the business is heading
• Make the data agent-ready: model and expose it (warehouse models, APIs, MCP connectors, permissions, governance) so AI agents, and ultimately our superagent, can consume it
• Build the retrieval layer for what is not in tables (documents, wiki content, tickets, meeting transcripts): chunking, embeddings, indexing. Agents working on outdated data give outdated and often wrong answers
• Engineer, don’t improvise: transformations are version-controlled, tested, and documented.
• Treat data quality as a product: monitoring, validation, and hygiene are built in,
• Team enablement: You partner with every team to get their data into the warehouse and into a state an agent can act on, and change how it gets created so machine-readable is the default
• In 6 months:
• 3 months: you own the warehouse and its ingestion landscape end to end, the datasets the reporting and forecasting cycle runs on reconcile, and you have a prioritized map of the company data the superagent is missing
• 6 months: Across Synera, data flows are documented, monitored, and trustworthy; team self-serve the answers they used to ask you for
• ⚡ In 12 months:
• 12 months: your data foundation becomes the knowledge layer of Synera’s superagent. AI agents across the company answer questions and execute work on data you made trustworthy

Benefits

• Powerful equipment - Synera empowers its team members with the equipment they need to do their best work.
• Company offsites - Synera is a remote/hybrid company - which is why it enjoys regrouping for two offsites per year.
• Company fitness & Jobrad - Healthy body, healthy mind. Synera enables its team members to keep up with their health!
• Development budget - Never stop learning: Synera wants its team members to grow and continuously learn new things!
• Team spirit - Synera enjoys spending time with each other and places a high value on functioning as a team - no siloes, no politics, and lots of laughs.
• Flexibility - As a remote/hybrid company, Synera offers all the flexibility of a modern company to attract exceptional talent.- You have built the data layer behind LLM applications: retrieval, embeddings and vector search, context design, MCP and comparable tool interfaces. Hands-on evidence beats a course
• You have built reporting and forecasting people actually use and know the difference between a dashboard and an answer
• SQL is second nature; you have built ETL/data pipelines (Airflow or similar) and data models and are strong in data extraction and transformation
• Ownership mentality: you document what you build, you notice what nobody asked about, and you would rather fix the system than the symptom
• An automator by instinct: scripts, APIs, workflow tools, and AI agents are how you scale yourself. You use LLMs as a daily working tool, not a novelty and the idea of building the data brain behind a company-wide AI agent excites you
• You have integrated multiple systems: APIs, webhooks, auth models, rate limits and unannounced schema changes. You can debug a discrepancy across three tools until you find which definition was wrong, and you think about data access and privacy by default
• You work in English confidently; German is a plus. You communicate clearly with non-technical colleagues, sales reps, marketers, and executives alike
• Even if you don’t meet these criteria perfectly but believe you have lots to bring to the role, we encourage you to apply. We know it’s tough, but please keep in mind that you don’t have to match all the listed requirements exactly to be considered for this role- Culture Interview (P&C)
• CRM Case Study
• Data Engineering Tech Interview
• Bar Raiser Interview (with the Founder)