Data Engineer

In our team, we have few Backend engineers and one ML engineer. We are hiring a Senior Data Engineer to build and own our analytics platform from the ground up.

Current Technology Stack

• Backend & data: Java/Spring, MySQL, MongoDB, Redis; tens of millions of behavioral/session records.
• Analytics & ML: Metabase (currently querying operational databases), Python, Polars, Parquet, CatBoost, FastAPI.
• Infrastructure: Docker, Kubernetes; DigitalOcean → AWS EKS migration; Terraform, Helm, Argo CD, ECR, Secrets Manager, Cloudflare.
• Data platform: No dedicated warehouse or orchestrator yet; technology selection is part of this role.

Some of our sites:
vinclarity.com - vehicle history reports

thathome.com - property records
efaq.com - psychological, educational, and just-for-fun quizzes

Key Responsibilities

• Design and implement a cost-effective analytical warehouse, ingestion pipelines, transformation models and orchestration.
• Integrate MySQL/MongoDB, payments, fraud, email/helpdesk, advertising APIs and quiz/session events; support CDC or incremental loads, retries and safe backfills.
• Own reliable models for subscriptions, revenue, cohorts, LTV, CAC and ROAS; improve Metabase performance and reduce analytical load on production.
• Implement data quality checks, monitoring, reconciliation, documentation, data contracts and access controls.
• Partner with the ML Engineer on reproducible, point-in-time correct training datasets; prevent data leakage and handle delayed labels.

Requirements

• 5+ years in Data Engineering; demonstrated ownership of production pipelines or a data platform.
• Advanced SQL and MySQL optimization; MongoDB/document data modeling; analytical and dimensional modeling.
• Strong Python with pandas or Polars, Parquet; ETL/ELT, incremental processing, idempotency, backfills and recovery.
• Production experience with a data warehouse and an orchestrator (e.g., Airflow, Dagster or Prefect); ability to choose appropriate tools.
• AWS fundamentals, Docker, Git/CI/CD; working familiarity with Kubernetes; pragmatic architectural judgment.
• English B2+.

Nice to have

• dbt; Redshift, ClickHouse, Snowflake or BigQuery; CDC/Debezium or Airbyte; subscription billing, marketing attribution and fraud data; Terraform/Helm/Argo CD; Java/Spring familiarity.

Conditions

• Fully remote setting
• Corporate devices (laptop, power station)
• Unlimited PTO (paid time off) policy
• Udemy courses