Role Description At Ford Motor Credit Company, we are modernizing our enterprise data warehouse in Google Cloud to enhance data, analytics, and AI/ML capabilities, improve customer experience, ensure regulatory compliance, and boost operational efficiencies. - Guide your team in conducting deep-dive analyses related to Current State Receivables and Originations data within our data warehouse. - Perform impact analysis related to Ford Credit North America's modernization efforts. - Provide strategic implementation solutions. - Lead cross-functional efforts, partnering closely with AI, data science, and product teams to develop innovative data solutions. - Coach and mentor direct reports, fostering professional growth and technical expertise. - Work directly with the Director of Data Engineering, collaborating on strategic direction and execution. Qualifications - Significant experience supporting North American teams. - Demonstrated leadership in designing, implementing, and operationalizing large-scale data warehouses, data lakes, and analytics platforms on Google Cloud Platform or other cloud environments. - Proven ability to lead and guide a team of data engineers. - Experience driving technical architecture decisions and making strategic technology choices within the Google Cloud Platform ecosystem and related third-party technologies. Requirements - Ability to design the right solutions with the appropriate combination of technologies for deployment on Google Cloud Platform.

Role Description We are looking for a skilled individual to join our rapidly growing team at Bluelight. This position is ideal for someone who thrives in a fast-paced, dynamic environment where everyone's opinions and efforts are valued and appreciated. You will have the opportunity to contribute to challenging and meaningful projects, developing high-quality applications that stand out in the market. We value continuous learning, personal growth, and hard work, offering a collaborative environment that promotes professional development. If you are passionate about software development and eager to be part of a growing software consultancy, we invite you to apply and join us on this exciting journey. - ETL Data Engineering: Develop and maintain ETL data engineering processes using Python (PySpark) within Azure Synapse Analytics Notebooks, and/or Azure Synapse Analytics Pipelines, to ensure efficient data extractions, transformation, and loading. - Data Warehousing: Apply your expertise in data warehousing, understanding star schemas, facts, and dimensions, to design and build effective data storage structures in a Massively Parallel Processing (MPP) SWL Pool. - Data Source Expertise: Extract data from various sources, including REST APIs, SWL database tables, and CSV files. - Azure Synapse Analytics Expertise: Utilize your deep knowledge of Azure Synapse Analytics to design and optimize data notebooks/pipelines for scalability and performance. - Data Fabric Concepts: Contribute to the implementation and understanding of other Data Fabric concepts, such as data lakes, lakehouses, delta lakes, and data cataloging, to enhance data management capabilities. - Data Modeling: Collaborate with data architects to create data models and schemas that align with business requirements. - Data Quality: Implement data quality checks and validation processes to maintain data accuracy and consistency. - Performance Tuning: Identify and resolve performance bottlenecks and optimize ETL data notebooks/pipelines to meet SLAs. - Monitoring and Troubleshooting: Monitoring ETL jobs, diagnose issues, and implement solutions to ensure data pipeline reliability. - Documentation: Maintain comprehensive documentation of ETL data engineering processes, data flows, and data transformations. - Collaboration: Work closely with cross-functional teams to understand data requirements and provide support for data-related initiatives. - Security and Compliance: Ensure data security and compliance with data governance and privacy standards. Qualifications - Bachelor’s degree in Computer Science, Information Technology, or a related field; or equivalent work experience, with certifications related to data engineering or data science (e.g. Azure Data Engineer) being a plus. - Proven experience in ETL data engineering with significant expertise in using Python (PySpark) to perform data extraction, transformation, and loading from REST APIs, SQL database tables, and CSV files. - Proficiency in using Azure Synapse Analytics resources including Notebooks, Pipelines, Linked Services, and Azure Key Vault. - Demonstrated ability to write complex SQL queries, optimize query performance, and work with both SparkSQL and MS SQL to effectively extract, transform, and load data. - Knowledge of data integration best practices and tools. - Experience with version control systems, such as Git (Azure DevOps). - Strong problem-solving and analytical skills, with a keen attention to detail. - Excellent communication skills, both verbal and written, with the ability to work collaboratively in a team environment with shifting priorities. - Familiarity with big data technologies, machine learning, and data analysis preferred. - Experience with data visualization tools (e.g. Power BI, Tableau) and Agile Methodologies a plus. Benefits - Competitive salary and bonuses, including performance-based salary increases. - Generous paid-time-off policy. - Flexible working hours. - Work remotely. - Continuing education, training, conferences. - Company-sponsored coursework, exams, and certifications. - Your contributions are highly valued by clients, and the work you do often has a direct and significant impact on their business. - You will have the opportunity to work on a variety of projects for our incredible clients, which will accelerate your career growth. - You’ll collaborate with modern technologies and work alongside some of the best professionals in the industry!

• Own End-to-End Delivery of Core Data Platform Components • Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse • Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others • Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness • Drive Technical Architecture • Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients • Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches • Evaluate and select technologies with a bias toward what ships well and scales sustainably • Build AI Evaluation Infrastructure • Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts • Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows • Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company • Ship with AI-Native Development Practices • Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates • Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability • Contribute to the team’s evolving practices around AI-accelerated SDLC • Establish Technical Standards • As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale • Help define what “good” looks like for a team building at speed without sacrificing quality • Mentor and provide technical guidance as the team grows from seed squad to full delivery organization • Collaborate Across Engineering and Product • Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans • Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective • Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

• Build and ship production-quality implementations of the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse • Write clean, well-tested, performant code across the full stack – backend services, data pipeline logic, and API integrations • Take end-to-end ownership of features from design through deployment, with accountability for correctness and reliability in production • Work closely with staff engineers to shape the architecture of a modern, AI-native data warehouse serving institutional financial clients • Bring thoughtful input on schema design, normalization approaches, and API patterns – and execute those decisions with precision • Identify and raise technical risks early; propose and implement solutions rather than waiting to be directed • Use agentic coding tools and LLM-assisted development as your primary workflow – this is how the entire team operates • Critically evaluate AI-generated code for correctness, edge cases, and regressions – shipping quality output regardless of how it was produced • Contribute to the team’s evolving practices around AI-accelerated development and testing • Build and maintain data validation checks, monitoring, and observability tooling that keeps the pipeline trustworthy at scale • Participate in on-call and production support, diagnosing and resolving data quality issues quickly and thoroughly • Write and maintain clear technical documentation for the systems you build • Partner effectively with staff and senior engineers, your engineering manager, and product management to translate requirements into well-scoped, executable work • Participate in design and code reviews, offering and receiving feedback constructively • Develop domain intuition around private markets data – fund administration, investment data schemas, institutional reporting – to make better technical decisions