Data Engineer

Magma Math, is a K–12 platform that helps teachers make smarter instructional decisions and encourages deeper student-driven discussions and collaboration around math.

We’re a fast-growing, well-funded company in thetop tier of European EdTech— backed by$40M Series Aand growing like crazy. But we’re keeping it lean, smart, and fun — without the corporate fluff.

Our work has areal impact: we’re helping students around the world get better at math, and we’rerecognized by education expertsfor improving how math is taught and learned.

This role isbased inWarsaw andwe also have officesinNew York, Stockholm, and London— you’ll have chances to meet everyone in person!

On-site role: We expect candidates to work from office 4 days a week.

What we’re looking for

We are looking for a Data Engineer to build and own the pipelines that our product and business decisions depend on. You will work on both real-time streaming and batch workloads, from ingestion through to a modelled warehouse layer that analysts and services query directly.

This is a hands‑on, infrastructure‑close role. You will design pipelines, write the Terraform that provisions them, and stay responsible for them in production. If you like owning data end to end rather than picking up tickets on someone else’s stack, this will suit you.

What you’ll work with

• Design, build and operate streaming pipelines with Kafka, Kinesis, Firehose and Flink

• Build and maintain batch ETL/ELT pipelines into Redshift

• Model and evolve our data warehouse — identify the underlying business goals and architect accordingly

• Work with analysts, backend engineers and product to turn requirements into reliable, well‑documented datasets

• Manage all data infrastructure as code with Terraform on AWS (S3, Lambda, SQS, Kinesis, Firehose, Redshift)

• Instrument pipelines with monitoring, alerting and data quality checks so problems surface before stakeholders notice them

• Take part in code review and keep our engineering standards high

Must have:

• 2–3+ years of commercial experience as a Cloud Data Engineer

• Apache Flink(Java or Python API) for stream processing

• Streaming platforms: Kafka and/or Kinesis, including practical understanding of partitioning, ordering, delivery guarantees and backpressure

• Data warehouse architectureexperience — you have designed a warehouse or a significant part of one, not only queried it

• Experience in adata‑critical environment— where data accuracy, freshness or latency directly affects revenue, compliance or user safety (fintech, adtech, e‑commerce at scale, healthcare, security, IoT or similar)

• Strong SQL, with hands‑on experience inAmazon Redshift(query tuning, distribution/sort keys, workload management)

• AWS: S3, Lambda, SQS, Kinesis, Firehose, Redshift

• Terraform— you provision your own infrastructure

• Gitand a collaborative branching/review workflow

• Familiarity with agentic development— you use AI coding agents and LLM‑based tooling as part of your daily workflow, and understand where to trust them and where to verify

• Pythonfor data engineering — production‑quality code, not just scripts

Nice to have:

• ClickHouse

• Node.js

• Apache Spark

• Experience with distributed architectures and their failure modes (consistency, partial failure, idempotency, exactly‑once vs at‑least‑once)

Our stack:

Redshift · ClickHouse · Python · Flink · Kafka · Kinesis · Firehose · Lambda · SQS · S3 · EKS · Terraform · ArgoCD · GitHub Actions · Quick Suite · Git

What we offer

• Salary up to24,000 PLN/month (+VAT)depending on seniority

• 26 days of leave covered by a yearly bonus

• 10 days of paid sick leave

• Yearlyteam meetupswith all the people in company

• GreatWarsaw office– full floor just for us with snacks, drinks, andtop‑tier coffee

• Multisport Plus card

• Table football and chill board game nights with pizza & beer

• Occasional movie nights

• We takeyour wellbeing very seriously — we want everyone to feel comfortable here

Recruitment Process:

1. 30‑min intro call

2. Technical interview (on site in office)

3. Culture fit interview in office

4. Interview with Product and Tech leaders

5. Reference check