The Lead Data Engineer is a seasoned professional role. Applies in-depth disciplinary knowledge, contributing to the development of new techniques and the improvement of processes and work-flow for the area or function. Integrates subject matter and industry expertise within a defined area. Requires in-depth understanding of how areas collectively integrate within the sub-function as well as coordinate and contribute to the objectives of the function and overall business. Evaluates moderately complex and variable issues with substantial potential impact, where development of an approach/taking of an action involves weighing various alternatives and balancing potentially conflicting situations using multiple sources of information. Requires good analytical skills in order to filter, prioritize and validate potentially complex and dynamic material from multiple sources. Strong communication and diplomacy skills are required. Regularly assumes informal/formal leadership role within teams. Involved in coaching and training of new recruits. Significant impact in terms of project size, geography, etc. by influencing decisions through advice, counsel and/or facilitating services to others in area of specialization. Work and performance of all teams in the area are directly affected by the performance of the individual.
Responsibilities:
- Helps to define and ongoing management of target data architecture for risk information.
- Liaises with other Citi risk organizations to identify and maintain appropriate alignment, specifically with Citi Data Standards.
- Works in conjunction with information owners and technology partners to define and implement the roadmap.
- Prepares materials for Monthly Operating Reviews (MORs), Portfolio Reviews, Horizontal meetings, Town Halls and Staff Meetings.
- Performs analysis of DQ issues and deliver metrics reporting.
- Supports Managers with status reports and presentation content. Guide data analysis and reporting processes that include collection from multiple sources, validation of data and assembly and presentation of required data.
- Develops new data collection and evaluation methodologies, including format design, data compilation, relevancy and metrics.
- Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
- Integrates in-depth data analysis knowledge with a solid understanding of industry standards and practices.
- Demonstrates a Good understanding of how data analytics teams and area integrate with others in accomplishing objectives.
- Applies project management skills.
- Applies analytical thinking and knowledge of data analysis tools and methodologies.
- Establish and create data pipelines for retention, cleanup, persistence, and maintenance of data on NoSQL datastores.
- Implement data consistency checks and rules to ensure data meets business standards and needs.
- Optimize data engineering pipelines and queries to ensure efficiency, reliability and scalability of data processing and analysis.
- Perform root cause analysis on issues and processes to answer specific business questions and identify opportunities for improvement.
Qualifications:
- 8+ years Banking or Financial Services experience
- Experience in analyzing and defining risk management data structures and architecture
- Demonstrated influencing, facilitation and partnering skills
- Track record of interfacing with and presenting results to senior management
- Analytical, flexible, team-oriented and have good interpersonal/communication skills
- Engineers with 8+years of data engineering experience in Big Data ecosystem with primary skill as Spark.
- Experienced in data integration (ETL/ELT) and building scalable and maintainable data pipeline supporting a variety of integration patterns (batch, replication, event streaming).
- Strong knowledge on NoSQL DB - HBase
- Should have basic knowledge on Bigdata Cluster and operations
- Support system migration programs — including legacy-to-cloud transitions — by conducting data mapping, reconciliation, and parallel run analysis
- Strong proficiency in Python and Spark Java with knowledge of core spark concepts (RDDs, Dataframes, Spark Streaming, etc) and Scala and SQL
- Data Integration, Migration & Large Scale ETL experience (Common ETL platforms such as PySpark/DataStage/AbInitio etc.) - ETL design & build, handling, reconciliation and normalization
- Experienced in working on medium to large enterprise projects, preferably in financial services
- Willingness to experiment and learn new approaches and technology applications
- Excellent written and verbal communication skills
Education:
- Bachelor’s/University degree or equivalent experience
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Data Architecture------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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