Lead Data Engineer

Job Description

Job Description Value Preposition Lead the development and maintenance of large-scale data platforms, data lakes and cloud solutions. Responsible for designing and building data pipelines for enterprise data through ETL/ELT processes. Job Details Position Title: Lead Data Engineer Career Level: P3 Job Category: Manager Role Type: Hybrid Job Location: Bangalore About the Team: The data engineering team is community of dedicated professionals committed to designing, building, and maintaining data platform solutions for the organization. Impact Enterprise data warehouse supports several critical business functions for the bank including Regulatory Reporting, Finance, Risk steering, and Customer 360. This role is vital for building and maintaining enterprise data platform, data processes, and to support business objectives. Our values — inclusivity, transparency, and excellence — drive everything we do. Join us and make a meaningful impact on the organization. Key Deliverables Responsible for designing, building, and maintaining data platform that supports data integrations for Enterprise Data Warehouse, Operational Data Store or Data Marts etc. with appropriate data access, data security, data privacy and data governance. Establish enterprise-scale data integration procedures, data pipelines and frameworks across the data development life cycle. Suggest and implement appropriate technologies to deliver resilient, scalable, and future-proof data solutions. Create data ingestion pipelines in data warehouses and other large-scale data platforms. Creating scheduled as well as trigger-based ingestion patterns using scheduling tools. Create performance optimized DDLs for any row-based or columnar databases such as Oracle, Postgres, Netezza database per Logical Data Model. Performance tuning of complex data pipelines and SQL queries. Performs impact analysis of proposed changes on existing architecture, capabilities, system priorities, and technology solutions. Manage deliverables of developers, perform design reviews and coordinate release management activities. Estimate and provide timelines for project activities. Identify, document, and communicate technical risks, issues and alternative solutions discovered during project. Drive automation, identify inefficiencies, optimize processes and data flows, and recommend improvements. Use agile engineering practices and various data development technologies to rapidly develop and implement efficient data products. Collaborate with Product Owners to understand PI goals, PI planning, requirement clarification, and delivery coordination. Technical support for production incidents and failures Work with global technology teams across different time zones (primarily US) to deliver timely business value. Skills and Qualification Functional Skills: Team Player: Support peers, team, and department management. Communication: Excellent verbal, written, and interpersonal communication skills. Problem Solving: Excellent problem-solving skills, incident management, root cause analysis, and proactive solutions to improve quality. Partnership and Collaboration: Develop and maintain partnership with business and IT stakeholders Attention to Detail: Ensure accuracy and thoroughness in all tasks. Technical/Business Skills: Data Engineering: 10+ years of experience in designing and building Data Warehouse and Data lakes. Good knowledge of data warehouse principles, and concepts. Technical expertise working in large scale Data Warehousing applications and databases such as Oracle, Netezza, Teradata, and SQL Server. Cloud-based data platform experience especially Snowflake and AWS. Data integration skills: Expertise in design and development of complex data pipelines Solutions using any industry leading ETL tools such as IBM Data Stage or SSIS or SAP Business Objects Data Services (BODS) Experience with Unix, Python or equivalent scripting language Basic knowledge of Transform/ELT tools such as DBT , Qlik Replicate, AWS Glue or Snowpark , etc. Expert in SQL , stored procedures and adept in tracing and resolving data integrity issues. Strong knowledge of data architecture, data design patterns, modeling Data Model : Expertise in Logical and Physical Data Model using Relational, Dimensional or Data Vault Modeling practices with high volume ETL/ELT processes. Good experience with scheduling tools such as Control-M Performance tuning of data pipelines and DB Objects to deliver optimal performance. Experience in Gitlab version control and CI/CD processes. Experience working in Financial Industry is a plus. Relationships & Collaboration Reports to: Associate Director - Data Engineering Partners: Senior leaders and cross-functional teams Leads: A team of Data Engineering associates Accessibility Needs We are committed to providing an inclusive and accessible hiring process. If you require accommodations at any stage (e.g. application, interviews, onboarding) please let us know, and we will work with you to ensure a seamless experience.