FBS Sr Data Engineer (SQL, Informatica)

Accountabilities

Data Engineering and Pipeline Development: Design, develop, and maintain scalable data pipelines for batch and streaming workloads, supporting both on-premises infrastructure and cloud environments.
SQL and Informatica Solutions: Build and optimize data integration workflows, transformations, and data processing solutions using SQL, Informatica, and other relevant technologies to meet business requirements.
Data Modeling and Architecture: Develop advanced data models, reusable data components, and data flows to support dimensional modeling, reporting, dashboard development, data feeds, and data science initiatives.
Business Requirements Translation: Collaborate with business and technical stakeholders to understand analytical needs, translate requirements into technical specifications, estimate workloads, and break down deliverables into manageable tasks and sprint plans.
Data Integration and API Development: Build and enhance API integrations to support increasing data volumes, greater complexity, and connectivity across enterprise systems and platforms.
Data Quality and Transformation: Prepare, cleanse, validate, and transform structured and unstructured data to ensure accuracy, consistency, and readiness for downstream analytics, reporting, and AI/ML applications.
Data Governance and Compliance: Apply data governance principles, established architectural standards, and code management practices to maintain secure, consistent, and well-managed data assets.
Reporting and Analytics Enablement: Deliver reliable datasets and access methods that support business users, reporting and visualization specialists, and data scientists in producing actionable insights.
Project Planning and Delivery: Independently manage data engineering projects of varying complexity, coordinate dependencies across teams, develop project plans, and deliver solutions within agreed timelines.
Technical Collaboration and Mentoring: Work with cross-functional teams to resolve technical challenges, contribute to architectural decisions, and provide guidance and support to junior associates when needed.
Continuous Improvement: Identify opportunities to improve pipeline performance, data reliability, integration processes, and the overall efficiency of data engineering practices.
Requirements
Professional Experience: Significant experience in data engineering or a related technical field, with the ability to independently deliver moderately complex to advanced data solutions.
SQL Expertise: Strong SQL skills, including writing and optimizing complex queries, transforming data, validating results, and troubleshooting data-related issues.
Informatica Experience: Hands-on experience with Informatica data integration and ETL/ELT workflows, including developing, maintaining, and optimizing data pipelines.
Data Pipeline Development: Experience building and maintaining scalable data pipelines for batch and streaming workloads across on-premises and cloud environments.
Data Modeling and Integration: Strong understanding of data modeling, data architecture, dimensional data, data mapping, API integrations, and structured and unstructured data processing.
Cloud and Database Technologies: Experience working with relational and non-relational databases, cloud platforms, and modern data engineering technologies.
Data Quality and Governance: Knowledge of data cleansing, validation, data governance principles, architectural standards, and best practices for maintaining reliable data assets.
Analytics and Reporting: Familiarity with reporting, visualization, dashboard development, and preparing data for advanced analytics or AI/ML applications.
Programming and Technical Problem-Solving: Ability to work with relevant programming languages and data processing techniques to develop robust, maintainable technical solutions.
Project Management and Agile Delivery: Experience translating business requirements into technical tasks, estimating effort, managing dependencies, and delivering work through structured project plans or Agile sprints.
Communication and Collaboration: Strong communication skills and the ability to work effectively with business stakeholders, data professionals, and technical teams across different functions.
Autonomy and Leadership: Ability to manage complex assignments independently, exercise sound technical judgment, and provide coaching or assistance to less experienced colleagues.
Adaptability: Comfortable working on projects with evolving requirements, multiple dependencies, and timelines that may require proactive planning and coordination.
Benefits
Competitive Compensation: Salary package aligned with experience, qualifications, and role responsibilities.
Performance-Based Bonuses: Opportunities to receive additional compensation based on performance.
Comprehensive Benefits Package: Access to a range of employee benefits.
Private Health Insurance: Healthcare coverage as part of the benefits package.
Paid Time Off: Time away from work to support personal well-being and work-life balance.
Flexible Work Arrangements: Remote and/or office-based options, depending on business requirements.
Training and Development: Opportunities to strengthen technical expertise and develop new skills through learning initiatives and partnerships with recognized organizations.
Global Work Environment: Collaboration with international teams in a diverse, inclusive, and multicultural setting.
Career Growth: Opportunities to expand your technical responsibilities, contribute to complex data initiatives, and develop leadership and mentoring skills.
Innovative Projects: Exposure to enterprise data engineering, analytics, cloud technologies, and evolving data practices.