The Senior Data Engineer will design, implement, and maintain data solutions on SQL Server (SSIS, SSAS, UC4 Atomic) while collaborating with stakeholders to ensure efficient data processing, storage, and retrieval. The role involves data pipeline development, performance optimization, troubleshooting, and ensuring data quality. The candidate should have expertise in SQL, SSIS, query optimization, and data engineering best practices, along with experience in onshore-offshore models, automation (IaC, Terraform, Jenkins), and cloud technologies.
What roles and responsibilities will be performed by the selected candidate?
Senior Data Engineer will be responsible for designing, implementing, and maintaining data solutions on SQL Server (SSIS, SSAS, UC4 Atomic)
Collaborate with various stakeholders, and ensuring the efficient processing, storage, and retrieval of large volumes of data
Technical Expertise and Responsibilities:
Should be able to design and build solutions in SSIS to extract, transform and load data into different source and target systems.
Should be able to analyze and understand the existing data landscape and provide recommendations/innovative ideas for rearchitecting / optimizing / streamlining to bring efficiency and scalability.
Must be able to collaborate and effectively communicate with onshore counterparts to address technical gaps, requirement challenges, and other complex scenarios.
Monitor and troubleshoot data systems to ensure high performance and reliability.
Should be highly analytical and detail-oriented with extensive familiarity with database management principles.
Optimize data processes for speed and efficiency.
Ensure the data architecture supports business requirements and data governance policies.
Define and execute the data engineering strategy in alignment with the company’s goals.
Integrate data from various sources, ensuring data quality and consistency.
Stay updated with emerging technologies and industry trends.
Understand the big picture business process utilizing deep knowledge in banking industry and translate them to data requirements.
Enabling and running data migrations across different databases and different servers
Perform thorough testing and validation to support the accuracy of data transformations and data verification used in machine learning models.
Analyze data and different systems to define data requirements.
Define data mapping working along with business and digital team and data team.
Data pipeline maintenance/testing/performance validation
Assemble large, complex data sets that meet functional / non-functional business requirements.
Analyze and identify gaps on data needs and work with business and IT to bring in alignment on data needs.
Troubleshoot and resolve technical issues as they arise.
Optimize data flow and collection for cross-functional teams.
Work closely with Data counterparts at onshore, product owners, and business stakeholders to understand data needs and strategies.
Collaborate with IT and DevOps teams to ensure data infrastructure aligns with overall IT architecture.
Implement best practices for data security and privacy.
Drive continuous improvement initiatives within the data engineering function
Optimize data flow and collection for cross-functional teams.
Understand impact of data conversions as they pertain to servicing operations.
Manage higher volume and more complex cases with accuracy and efficiency.
What is the expectation from the candidate’s current role/profile?
Should be proficient in SSIS, SQL and Query optimization.
Expertise in SQL knowledge and experience working with relational databases.
Should have worked in onshore offshore model managing challenging scenarios.
Ability to work with ambiguity and vague requirements and transform them into deliverables.
Good combination of technical and interpersonal skills with strong written and verbal communication; detail-oriented with the ability to work independently.
Drive automation efforts across the data analytics team utilizing Infrastructure as Code (IaC) using Terraform, Configuration Management, and Continuous Integration (CI) / Continuous Delivery (CD) tools such as Jenkins.
Help build define architecture frameworks, best practices & processes. Collaborate on Data warehouse architecture and technical design discussions.
Expertise in Python will be of added advantage.
Experience in data bricks will be of added advantage.
Should have expertise in data life cycle, data ingestion, transformation, data loading, validation, and performance tuning.
SQL, SSIS, SSAS, UC4 Atomic, Query Optimization, Data Architecture, Data Transformation, ETL, Data Pipeline, Onshore-Offshore Collaboration, Data Governance, Security, and Privacy, Terraform, Jenkins, CI/CD, Automation, Data Warehousing, Performance Tuning, Python, Databricks (Preferred), Banking Domain Knowledge (Preferred).