8 of 8 Great Expectations Jobs in London

Azure Data Factory, Synapse Analytics, or ADLS . Proficiency in SQL and scripting languages (e.g., Python, Scala) for data validation and test automation. Experience with test automation frameworks (e.g., Great Expectations, DBT tests, PyTest, or similar ). Exposure to CI/CD testing within Azure DevOps, GitHub Actions, or Jenkins . Understanding of data governance, data security, and More ❯

Engines: Strong with RDBMS (PostgreSQL, MySQL), NoSQL (DynamoDB, Cassandra), data lakes (Parquet, ORC), and warehouse paradigms. Observability & Quality: Deep familiarity with metrics, logging, tracing, and data quality tools (e.g., Great Expectations, Monte Carlo, custom validation/test suites). Security & Governance: Data encryption, secrets management, RBAC/ABAC, and compliance awareness (GDPR, CCPA). CI/CD for More ❯

environment. • Model data using Kimball/star schemas and data vault principles to support BI and self service analytics. • Implement data quality, lineage, and observability tooling (e.g., dbt tests, Great Expectations, Azure Purview). • Optimise storage and compute costs through partitioning, incremental loads, and automation. • Collaborate with DevOps to embed CI/CD and Infrastructure as Code (Terraform More ❯

needs. Experience with data ingestion tools, like Fivetran. Advantageous Exposure to deploying applications with Kubernetes. Experience with Data Orchestrator tools (Airflow, Prefect, etc.) Experience with Data Observability tools (Montecarlo, Great Expectations, etc.) Experience with Data Catalog tools (Amundsen, OpenMetadata, etc.) Interview Process Call with the talent team Take home task Tech interview CPTO interview Life at Lendable The More ❯

MLflow or other model lifecycle tools Effective communicator and trainer - able to help others upskill Comfortable building internal tools and documentation Nice to Have: Experience with Terraform, dbt, or Great Expectations Exposure to software engineering best practices in a collaborative environment Knowledge of data governance and compliance practices Benefits : In addition to a competitive salary, IWSR offers Generous More ❯

and managing a feature store , and driving best practices for feature engineering in production systems . - Proficiency in model testing strategies , including unit testing for pipelines, data validation (e.g., Great Expectations, Deequ), and A/B or shadow testing. - Experience with model monitoring frameworks - Solid knowledge of CI/CD for ML , including automated training and deployment workflows. More ❯

enforce data quality, lineage, and contracts during platform migration. Partner with engineers and architects to build scalable, validated pipelines . Integrate tooling across Alation, Azure Data Services, Databricks , and Great Expectations . Align with Data Governance to support data domain stewards during migration. 💼 You Bring: Proven track record delivering data platform and migration initiatives as a Product Manager. More ❯

enforce data quality, lineage, and contracts during platform migration. Partner with engineers and architects to build scalable, validated pipelines . Integrate tooling across Alation, Azure Data Services, Databricks , and Great Expectations . Align with Data Governance to support data domain stewards during migration. 💼 You Bring: Proven track record delivering data platform and migration initiatives as a Product Manager. More ❯