Data Scientist / Data Engineer (Analytics + Data Pipelines)

Data Scientist / Data Engineer

• Design and build end-to-end

  data pipelines

  (batch and, if applicable, streaming).
• Collect, clean, transform, and model data into well-structured datasets for analytics and ML.
• Develop and maintain a

  data warehouse/lake

  model (dimensional modeling, data marts, curated layers).
• Implement

  data quality checks

  , observability, lineage, and monitoring.
• Perform exploratory analysis and deliver insights via dashboards, notebooks, and stakeholder-ready summaries.
• Build and deploy ML models when needed (forecasting, churn/segmentation, anomaly detection, recommendations).
• Run experiments / A/B testing support (metrics definitions, evaluation, statistical validity).
• Collaborate with backend teams to define event schemas, tracking plans, and data contracts.
• Optimize performance and cost across storage, compute, and queries.

• Strong SQL and solid programming skills (Python preferred).
• Experience building pipelines using tools like

  Airflow / Dagster / Prefect

  (or equivalent).
• Strong knowledge of data modeling (star schema, slowly changing dimensions, event modeling).
• Experience with at least one of:

  PostgreSQL / MySQL / BigQuery / Snowflake / Redshift

  .
• Proven ability to validate data correctness and implement

  data quality frameworks

  .
• Comfortable communicating insights and technical trade-offs to non-technical stakeholders.

• Streaming:

  Kafka / Kinesis / PubSub

  , real-time processing (

  Spark Streaming / Flink

  ).
• Big data:

  Spark

  , distributed compute, partitioning strategies.
• Lakehouse:

  Iceberg / Delta / Hudi

  , object storage (S3/GCS/Azure Blob).
• MLOps:

  MLflow

  , model monitoring, feature stores, deployment pipelines.
• BI:

  Superset / Power BI / Looker / Metabase

  , semantic layers.
• Cloud: AWS/Azure/GCP (IAM, networking basics, managed data services).
• Experience with privacy/security compliance (PII handling, retention policies, access controls).

• Ownership: you build reliable systems, not just one-off scripts.
• Curiosity: you ask the “why” behind metrics and propose better approaches.
• Practicality: you can balance speed vs correctness and deliver iteratively.
• Strong collaboration with engineers, product, and leadership.