Bengaluru, Karnataka, India
Actively hiring, first seen by us todayEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Build and maintain AWS data lake tables and curated datasets using dbt and Trino
Maintain Apache Airflow workflows that automate performance monitoring data processing
Create dbt transformation rules, blueprint files, and data dictionaries
Implement data quality checks using dbt expectations and reusable macros
Translate model monitoring requirements into data requirements and transformation logic
Design, build, and maintain scalable Amazon Web Services-based data lake tables and curated datasets using dbt and Trino
Build and maintain Apache Airflow workflows to automate end-to-end ongoing performance monitoring data processing
Create and maintain dbt transformation rules and supporting governance artifacts
Implement, govern, and operationalize data quality standards for ongoing performance monitoring datasets
translate ongoing performance monitoring and model monitoring requirements into clear data requirements, transformation logic, and consumable datasets
Required qualifications, capabilities, and skills - Hold a Bachelor’s degree in a relevant field (for example, Computer Science, Engineering, Information Systems, or Statistics) and have 7+ years of experience in data engineering, data management, analytics, or related roles. - Demonstrate a strong understanding of model risk concepts and the needs of ongoing performance monitoring, with the ability to translate monitoring and control requirements into resilient data models and pipelines.
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- Apply advanced SQL skills (window functions, joins, common table expressions, and complex aggregations), including performance tuning and building scalable analytical datasets. - Bring strong hands-on experience with Amazon Web Services data platforms and modern engineering tooling, including dbt, dbt expectations, Trino, Python, and GitHub (or equivalent software development lifecycle tooling). - Show proven experience building scalable dbt models using incremental processing techniques and designing reusable dbt macros to improve consistency, maintainability, and auditability.
- Have experience with model monitoring datasets and patterns such as performance metrics, drift/stability monitoring, segmentation, thresholds, and outcomes/labels alignment (where applicable to the model type). - Bring experience with Python data libraries (for example, pandas and numpy) and descriptive statistics concepts supporting monitoring and data quality analysis. - Demonstrate proficiency with Jira, Confluence, and Microsoft Office.
- Demonstrate a strong understanding of model risk concepts and the needs of ongoing performance monitoring, with the ability to translate monitoring and control requirements into resilient data models and pipelines. - Apply advanced SQL skills (window functions, joins, common table expressions, and complex aggregations), including performance tuning and building scalable analytical datasets. - Bring strong hands-on experience with Amazon Web Services data platforms and modern engineering tooling, including dbt, dbt expectations, Trino, Python, and GitHub (or equivalent software development lifecycle tooling).
Job summary As a Senior Associate in the International Consumer Banking Risk Data Office, you will assume ownership of the current set of risk modeling data models and views, including the setup and maintenance of modeling-specific data tables. You will create and maintain dbt transformation rules, blueprint files, and data dictionaries; automate key processes; maintain Apache Airflow workflows supporting the ongoing performance monitoring process; and deliver required data reporting. You will collaborate with model risk stakeholders and technology teams to translate monitoring requirements into clear data requirements, transformation logic, and consumable datasets. Job responsibilities
- Collaborate with model risk stakeholders (for example, model owners, validators, ongoing performance monitoring leads, risk data owners, and technology teams) to translate ongoing performance monitoring and model monitoring requirements into clear data requirements, transformation logic, and consumable datasets. - Design, build, and maintain scalable Amazon Web Services-based data lake tables and curated datasets using dbt and Trino, aligned to Medallion architecture (Bronze/Silver/Gold) and best-practice modeling approaches for monitoring, analytics, and reporting. - Create and maintain dbt transformation rules and supporting governance artifacts, including developing blueprint files for attribute tagging (for example, ongoing performance monitoring metrics, thresholds, segmentation attributes, critical data elements, and sensitivity/classification where applicable) and maintaining data dictionaries and business/technical definitions, along with end-to-end data lineage documentation and logic traceability to model monitoring requirements.
- Design, build, and maintain scalable Amazon Web Services-based data lake tables and curated datasets using dbt and Trino, aligned to Medallion architecture (Bronze/Silver/Gold) and best-practice modeling approaches for monitoring, analytics, and reporting. - Create and maintain dbt transformation rules and supporting governance artifacts, including developing blueprint files for attribute tagging (for example, ongoing performance monitoring metrics, thresholds, segmentation attributes, critical data elements, and sensitivity/classification where applicable) and maintaining data dictionaries and business/technical definitions, along with end-to-end data lineage documentation and logic traceability to model monitoring requirements. - Analyze, onboard, and integrate new data sources required for ongoing performance monitoring (model inputs, outputs, outcomes, performance labels, and operational/process data), including source profiling, quality assessment, and mapping to monitoring metrics.
- Analyze, onboard, and integrate new data sources required for ongoing performance monitoring (model inputs, outputs, outcomes, performance labels, and operational/process data), including source profiling, quality assessment, and mapping to monitoring metrics. - Implement, govern, and operationalize data quality standards for ongoing performance monitoring datasets (completeness, accuracy, timeliness, uniqueness, and referential integrity) using dbt expectations and reusable dbt macros to support consistency, maintainability, and auditability. - Build and maintain Apache Airflow workflows to automate end-to-end ongoing performance monitoring data processing, including orchestration, scheduling, dependency handling, backfills, monitoring/alerting, and operational runbooks.
- Bring strong hands-on experience with Amazon Web Services data platforms and modern engineering tooling, including dbt, dbt expectations, Trino, Python, and GitHub (or equivalent software development lifecycle tooling). - Show proven experience building scalable dbt models using incremental processing techniques and designing reusable dbt macros to improve consistency, maintainability, and auditability. - Communicate effectively across Risk and Technology teams, with experience working in Agile environments.
- Implement, govern, and operationalize data quality standards for ongoing performance monitoring datasets (completeness, accuracy, timeliness, uniqueness, and referential integrity) using dbt expectations and reusable dbt macros to support consistency, maintainability, and auditability. - Build and maintain Apache Airflow workflows to automate end-to-end ongoing performance monitoring data processing, including orchestration, scheduling, dependency handling, backfills, monitoring/alerting, and operational runbooks. - Automate key processes supporting model monitoring (for example, metric dataset refreshes, segmentation datasets, challenger/benchmark feeds, and monitoring aggregations) to improve control, reduce manual effort, and increase scalability.
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New Analyst postings at the employers we follow are up 24% over the last 6 weeks compared with the 6 before.
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We file this job under Analyst, by the work the posting describes.
Analysts answer business questions with data. They pull numbers with SQL, build reports and dashboards, and explain what changed and why to the people making decisions.
Most postings ask for SQL, spreadsheets, a dashboard tool such as Tableau or Power BI, and clear writing.
Unlike a data scientist, an analyst mostly explains what happened rather than building models that predict what will.
Job summary
As a Senior Associate in the International Consumer Banking Risk Data Office, you will assume ownership of the current set of risk modeling data models and views, including the setup and maintenance of modeling-specific data tables. You will create and maintain dbt transformation rules, blueprint files, and data dictionaries; automate key processes; maintain Apache Airflow workflows supporting the ongoing performance monitoring process; and deliver required data reporting. You will collaborate with model risk stakeholders and technology teams to translate monitoring requirements into clear data requirements, transformation logic, and consumable datasets.
Job responsibilities
Required qualifications, capabilities, and skills
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Bengaluru, Karnataka, India
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