Software Engineer - Data & Full Stack (f/m/x)

This role sits inside our 5-person Engineering team, running the high-volume data platform behind our mobile OEM advertising business: nightly ETL pipelines pulling from 10+ mobile attribution and ad-network partners (AppsFlyer, Adjust, Everflow, Kochava, Huawei, Xiaomi, Oppo, Vivo, Mintegral), an Amazon Redshift warehouse feeding BI, and our internal full-stack platform (APH) used daily by our media buyers and partner managers.

As our Software Engineer, you'll work across data pipelines, the warehouse, and the internal product, with real ownership from day one. Concretely, that means:

• Building and maintaining Python ETL pipelines orchestrated on Apache Airflow (Amazon MWAA), running on ECS Fargate: API ingestion, S3 staging, Redshift COPY loads
• Developing and evolving our Redshift star-schema warehouse: fact/dimension modeling, SQL transformation jobs, Flyway schema migrations, query performance tuning (DISTKEY/SORTKEY), data quality and reconciliation checks
• Contributing full stack to our internal platform: Spring Boot 3 / Java 21 backend (WebFlux, R2DBC/PostgreSQL, Redshift analytics reads) and Vue.js frontend (Nuxt, Vuetify)
• Adding integrations with new ad-network and attribution partners end to end: API client → transformer → S3 → Redshift → reporting
• Owning reliability: idempotent re-runnable jobs, structured logging, monitoring/alerting, runbooks, and a light on-call rotation
• Reviewing teammates' PRs and writing reviewable, well-tested changes yourself

Must haves (these are non-negotiable for day one):

• 3–5 years professional software engineering experience with production code in at least two of: Python, Java, SQL-heavy data work — and genuine willingness to work in all three
• Strong SQL — comfortable with window functions, CTEs, large joins; experience with a columnar/MPP warehouse (Redshift, Snowflake, BigQuery) or deep PostgreSQL experience and the curiosity to learn Redshift's differences
• Python for data pipelines — API clients, pagination/rate-limit handling, pandas or plain transforms, pytest
• AWS fundamentals — S3, IAM basics, and at least one of ECS/Lambda/EC2 in production
• Experience with batch/scheduled data workflows (Airflow preferred; Dagster/Prefect/cron-based acceptable) and an instinct for idempotency and failure handling
• Can read and modify a Java or similar typed backend codebase (Spring Boot a plus; C#/Kotlin backgrounds transfer fine)

Nice-to-haves (valuable, but not required):

• Amazon MWAA specifically, or self-managed Airflow at scale
• Redshift specifics: COPY/UNLOAD, DISTKEY/SORTKEY design, WLM, stl_/svl_ system tables
• Spring WebFlux / reactive Java, R2DBC
• Vue.js/Nuxt experience (React experience transfers)
• Terraform/Terragrunt, Docker, GitHub Actions