Applied Data Engineering Lead

3 days ago

nadiad, India beBeeDataScientist Full time

About the RoleTransform data insights into actionable signals that seamlessly integrate with your ad stack.We unify your first-party data, predict lifetime value, activate high-value signals, and prove revenue impact—governed, auditable and real-time.This role is for a builder-owner who loves taking ownership, accountability and thrives in 0 → 1 execution.Owning the entire end-to-end ML pipeline and orchestration layer — from raw data → features → models → deployment → monitoring → activation.Main Responsibilities:Data plumbing & schemas: Ingest and model data across various 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 and model registry.ML pipeline & training: Stand up reproducible training pipelines with experiment tracking, hyperparameter search, and evaluation.MLOps & deployment: Package models, deploy to managed services, wire CI/CD, and blue-green/rollback patterns.Orchestration: Schedule jobs with Airflow/Prefect; manage dependencies, SLAs, retries; design backfills and incremental loads.Observability: Implement DQ checks, model performance & drift monitoring, alerting, and cost guardrails.Activation & APIs: Ship fast, robust services to push signals into external systems or via reverse ETL.Experimentation & measurement: Partner with product/GTM on uplift tests, holdouts, and calibration to prove incremental value.Requirements:7-10+ years shipping DS/ML systems to production (not just notebooks).Fluent in Python, SQL, and one major cloud, with BigQuery/Snowflake and dbt experience.MLOps (MLflow, Feast) and orchestration (Airflow, Prefect) expertise.Ability to design measurement plans and communicate trade-offs with clarity.Thriving in ambiguity, moving fast, and owning outcomes end-to-end.Benefits:Founding-team impact, meaningful equity, and the chance to build the ML backbone of a category-defining product.Nice to Have:Streaming, privacy/consent, clean rooms experience.

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