Required skills:

Advanced statistics and ML modeling (hypothesis testing, experimentation, feature engineering, evaluation, calibration). Strong SQL/Python for large-scale data wrangling (joins, windows, tuning, cleansing, reconciliation, automated quality checks).

Data engineering for scalable batch/streaming pipelines (ETL/ELT, CDC, incremental, Airflow/Prefect/Dagster).

Deep knowledge of modern data platforms (lakehouse/warehouse, S3/ADLS, Snowflake/BigQuery/Redshift, Parquet/Delta/Iceberg, partitioning, access control). MLOps deployment with Docker, Kubernetes, CI/CD, MLflow, and end-to-end monitoring.

Nice to have skills:

"Build and maintain production-grade data pipelines (batch and streaming) with clear SLAs, retries, idempotency, and automated backfills.

Integrate and model data across sources by defining schemas, keys, transformations, and curated layers that support analytics and ML consumption.

Implement data quality, observability, and governance controls, including validation rules, lineage, access controls, and anomaly detection on data freshness and volume.

Productionize analytics and ML by packaging models, deploying services or jobs, managing versioning, and monitoring drift, performance, and latency.

Serve trusted data products to consumers via optimized warehouse tables, feature stores, and APIs, ensuring secure access and predictable query performance."