Data Engineer and Compliance lead - VP- PUNE

The Data Engineering Technology Lead is a senior level position responsible for establishing and implementing new or revised data platform eco systems and programs in coordination with the Technology team. The overall objective of this role is to lead data engineering team to implement the business requirements: The Data Engineering Technology Lead is a senior level position responsible for establishing and implementing new or revised data platform eco systems and programs in coordination with the Technology team. The overall objective of this role is to lead data engineering team to implement the business requirements:
Responsibilities:

• Design, build and maintain batch or real-time data pipelines in data platform. 
• Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources.
• Develop ETL (extract, transform, load) processes to help extract and manipulate data from multiple sources. 
• Automate data workflows such as data ingestion, aggregation, and ETL processing. 
• Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders. 
• Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production. 
• Build, maintain, and deploy data products for analytics and data science teams on data platform
• Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.
• Monitor data systems performance and implement optimization strategies.
• Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership.
• Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements
• Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards
• Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint
• Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation
• Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals
• Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions
• Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary
• 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.
• Documentation Data Lineage: Data Requirements for various data governance related use cases within Citi Information Security Office. Documenting data models, data dictionaries, and data flow diagrams. 
• Complete fine grain Lineage from Consumer Boundary to Source. Document the whole lineage within Citi approved tools.
• Implement high-level data flow diagrams from Citi stakeholders to understand data movement, including EUCs and Models as applicable.
• Conduct meetings with key stakeholders to gather insights and understand end-to-end data flows and data structures with multiple applications/systems.
• Conduct gap analysis of the decomposed products/report against the data element level lineage documented and flag gaps for remediation.
•  Assess relevant data quality controls for supporting processes for all critical data elements.
•  Understanding and Working with Data Models: Understanding business needs and translation into logical and physical data models, ensuring data integrity and consistency.
•  Data Analysis and Profiling: Analyzing data sources, identifying data issues, and ensuring data quality. 

Qualifications:

• 12+ years of total experience Data engineering role
• Practical problem solving and strategic thinking skills
• Demonstrated leadership, interpersonal skills and relationship building skills
• Service oriented attitude
• Ability to work in a fast-paced environment
• Experience working or leading requirement gathering efforts for multiple large development projects at one-time
• Proficient using basic technical tools and systems
• Good interpersonal and communication skills

Education:

• Bachelor’s/University degree, Master’s degree preferred

The position has 2 key job responsibilities

Data Engineering

• Building Data Pipelines: Creating systems for collecting, storing, and transforming data from various sources. 
• Data Collection and Management: Data engineers are responsible for gathering data from various sources, ensuring its quality, and making it accessible for analysis. 
• Data Transformation: They convert raw data into usable formats, often using ETL (Extract, Transform, Load) processes, to make it suitable for analysis and reporting. 

Data Governance and Compliance

• Documentation Data Lineage: Data Requirements for various data governance related use cases within Citi Information Security Office. Documenting data models, data dictionaries, and data flow diagrams. 
• Complete fine grain Lineage from Consumer Boundary to Source. Document the whole lineage within Citi approved tools.
• Implement high-level data flow diagrams from Citi stakeholders to understand data movement, including EUCs and Models as applicable.
• Conduct meetings with key stakeholders to gather insights and understand end-to-end data flows and data structures with multiple applications/systems.
• Conduct gap analysis of the decomposed products/report against the data element level lineage documented and flag gaps for remediation.
•  Assess relevant data quality controls for supporting processes for all critical data elements.
•  Understanding and Working with Data Models: Understanding business needs and translation into logical and physical data models, ensuring data integrity and consistency.
•     • Data Analysis and Profiling: Analyzing data sources, identifying data issues, and ensuring data quality. 

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