Responsibilities
- Support new and existing clients with their data engineering requirements as part of a dedicated team.
- Advise clients on optimal technical approaches to meet their business objectives.
- Manage and monitor progress across multiple client engagements, ensuring timely tracking and reporting.
- Develop, deploy, and maintain sophisticated data pipelines, fix data issues, implement transformations, and suggest improvements for data quality and integrity.
- Evaluate data sources for relevance and propose which datasets should be integrated into analytics workflows.
- Apply foundational data management principles such as data governance, security protocols, and quality assurance in all solutions.
- Work closely with internal and cross-functional teams to deliver data products and train end users on analytical tools and environments.
- Investigate, diagnose, and resolve data-related defects and system incidents of moderate complexity.
- Validate the accuracy and reliability of data flows, transformation logic, and data components through rigorous testing.
Requirements
- Minimum of five years of hands-on experience in data engineering or data modeling, ideally with direct work on the Databricks platform.
- Proficiency with one or more technologies such as Spark, Hadoop, Kafka, Databricks, pandas, scikit-learn, HPO, or data warehousing systems like SQL and OLTP/OLAP/DSS.
- Demonstrated expertise in data modeling, including dimensional modeling and either Data Vault or third normal form, especially when working with disorganized source data.
- Solid grasp of the complete data analytics lifecycle from ingestion to insight.
- Proven ability to troubleshoot and solve technical problems, including debugging code and data workflows.
- Clear and effective communication skills, both spoken and written.
- Intermediate leadership capabilities with a history of self-driven learning and development.
- Strong organizational skills with the ability to manage time and prioritize tasks efficiently.
Nice to Have
- Experience in data engineering or machine learning operations is highly advantageous.
- Familiarity with public cloud infrastructure such as AWS, Azure, or Google Cloud Platform is beneficial.
- Industry background in Financial Services, particularly within banking, is preferred.
- Knowledge of regulatory compliance standards and requirements in banking IT environments.
- Databricks Certification is a nice-to-have credential.
Benefits
- Innovative Environment: Engage with advanced technologies and leaders in data engineering and artificial intelligence.
- Customer Impact: Help organizations transform how they use data for strategic decisions.
- Career Growth: Access professional development and pathways for advancement.
- Collaborative Culture: Become part of a team that emphasizes teamwork and shared knowledge.
Responsibilities
- Support new and existing clients with their data engineering requirements as part of a dedicated team.
- Advise clients on optimal technical approaches to meet their business objectives.
- Manage and monitor progress across multiple client engagements, ensuring timely tracking and reporting.
- Develop, deploy, and maintain sophisticated data pipelines, fix data issues, implement transformations, and suggest improvements for data quality and integrity.
- Evaluate data sources for relevance and propose which datasets should be integrated into analytics workflows.
- Apply foundational data management principles such as data governance, security protocols, and quality assurance in all solutions.
- Work closely with internal and cross-functional teams to deliver data products and train end users on analytical tools and environments.
- Investigate, diagnose, and resolve data-related defects and system incidents of moderate complexity.
- Validate the accuracy and reliability of data flows, transformation logic, and data components through rigorous testing.
Required
- Minimum of five years of hands-on experience in data engineering or data modeling, ideally with direct work on the Databricks platform.
- Proficiency with one or more technologies such as Spark, Hadoop, Kafka, Databricks, pandas, scikit-learn, HPO, or data warehousing systems like SQL and OLTP/OLAP/DSS.
- Demonstrated expertise in data modeling, including dimensional modeling and either Data Vault or third normal form, especially when working with disorganized source data.
- Solid grasp of the complete data analytics lifecycle from ingestion to insight.
- Proven ability to troubleshoot and solve technical problems, including debugging code and data workflows.
- Clear and effective communication skills, both spoken and written.
- Intermediate leadership capabilities with a history of self-driven learning and development.
- Strong organizational skills with the ability to manage time and prioritize tasks efficiently.
Preferred
- Experience in data engineering or machine learning operations is highly advantageous.
- Familiarity with public cloud infrastructure such as AWS, Azure, or Google Cloud Platform is beneficial.
- Industry background in Financial Services, particularly within banking, is preferred.
- Knowledge of regulatory compliance standards and requirements in banking IT environments.
- Databricks Certification is a nice-to-have credential.
Benefits
- Innovative Environment: Engage with advanced technologies and leaders in data engineering and artificial intelligence.
- Customer Impact: Help organizations transform how they use data for strategic decisions.
- Career Growth: Access professional development and pathways for advancement.
- Collaborative Culture: Become part of a team that emphasizes teamwork and shared knowledge.
