Expert Data Engineer

1 week ago

thrissur, India beBeeData Full time

Job OverviewThe role involves owning the entire end-to-end machine learning pipeline and orchestration layer from raw data to features to models to deployment to monitoring to activation.You will be responsible for owning data plumbing & schemas, feature engineering, ML pipeline & training, MLOps & deployment, orchestration, observability, activation & APIs, experimentation & measurement, and documentation.The ideal candidate should have 7-10+ years of experience shipping DS/ML systems to production, be fluent in Python, SQL, and one major cloud, and have experience with BigQuery/Snowflake and dbt. They should also know MLOps and orchestration concepts.Key Responsibilities:Data Plumbing & Schemas: Ingest and model data across platforms; define event taxonomy, identity graph, and data contracts.Feature Engineering: Build offline/online features for pLTV, propensity, and clustering; ensure parity with a feature store (e.g., Feast) and model registry (e.g., MLflow).ML Pipeline & Training: Stand up reproducible training pipelines (Python/SQL/dbt) with experiment tracking, hyperparameter search, and evaluation.MLOps & Deployment: Package models (Docker), deploy to managed services, wire CI/CD (GitHub Actions) and blue-green/rollback patterns.Orchestration: Schedule jobs with Airflow/Prefect; manage dependencies, SLAs, retries; design backfills and incremental loads.Observability: Implement DQ checks (Great Expectations/dbt tests), model performance & drift monitoring, alerting, and cost guardrails.Activation & APIs: Ship fast, robust services (FastAPI/Cloud Functions) to push signals into APIs or via reverse ETL (Hightouch/Census).Experimentation & Measurement: Partner with product/GTM on uplift tests, holdouts, and calibration to prove incremental value.Success Metrics:30 days: Audit current data + tracking; publish V1 tracking plan & data model; baseline pLTV/propensity notebook and EDA; quick wins on data reliability.60 days: Production warehouse models (dbt) + orchestrated pipelines; first model in staging with offline/online validation; reverse ETL wired for a design partner.90 days: v1 serving in prod with monitoring; value-based bidding pilot live in Google/Meta for 2+ partners; dashboarding for KPIs and model health; draft case study.

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