Role Description The Data Engineering & AI/ML capability serves as the backbone of our organization's data-driven operations. We are seeking a talented and motivated Senior Data Engineer to join our dynamic Data Engineering team. As a key member of our engineering team, you will play a crucial role in: - Constructing and optimizing data pipelines - Implementing efficient storage solutions - Orchestrating the infrastructure necessary to support our customer’s data-driven initiatives - Leading engineering engagements by collaborating with cross-functional teams including customers, partners, and internal engineers Location: Primarily Remote. Candidates must be based in CA or DC Metro Area for proximity to project and client teams. Security Clearance Requirement: Applicants must be US citizens and eligible to obtain and maintain an active Secret security clearance or above. Key Responsibilities - Data Collection and Processing - Acquire, clean, and preprocess diverse datasets from various sources, establishing best practices and ensuring data quality standards. - Design and build required infrastructure for optimal extraction, transformation and loading of data from various data sources using CSP managed services and SQL technologies, focusing on scalability and cost efficiency. - Develop, maintain, and optimize mission-critical data pipelines to ensure a continuous flow of high-quality data, implementing robust monitoring and alerting. - Data Migrations & Optimization - Architect and develop data migration strategies and schemas to lead complex customer migrations from on-prem to cloud technologies. - Oversee and execute high-volume data migration activities, ensuring data integrity and minimal downtime. - Optimize databases and data warehouses for efficient querying and data storage, implementing advanced partitioning, indexing, and tuning techniques. - Data Analysis and Visualization - Perform exploratory data analysis to uncover patterns, trends, and insights, providing actionable recommendations to technical and business stakeholders. - Create visualizations and reports to communicate findings effectively to stakeholders both internally and externally, driving data-driven decision making. - Collaboration and Documentation - Collaborate with cross-functional teams, including software engineers, domain experts, and business analysts, to understand requirements and deliver integrated solutions, end-to-end data solutions. - Create and maintain comprehensive documentation for data architectures, code, algorithms, and models. Ensure that the knowledge is shared and accessible within the team. - Customer Engagement - Act on client feedback constructively to improve services and outcome, serving as a key technical contact for data-related discussions. - Continuously seek ways to enhance the overall customer experience, proactively identifying and addressing complex data challenges. - Continuous Learning and Innovation - Stay updated on the latest developments in cloud data services, machine learning, data science, and analytics. - Drive innovation by evaluating, proposing, and implementing cutting-edge techniques and technologies to address client challenges. Qualifications - Expert-level understanding and experience with SQL and relational database concepts - Deep understanding of database technologies, data warehouses, and ETL tools (e.g., MySQL, PostgreSQL, Beam, Airflow, and Kafka) - Proven track record of designing and implementing scalable data pipelines - Advanced experience with data analysis tools (e.g., Jupyter, Colab, Pandas) - Extensive experience with data visualization tools (e.g., Tableau, Looker, PowerBI, Qlik, and SuperSet) - Demonstrated experience developing comprehensive data strategies and facilitating data migrations into production systems - Expertise with cloud platforms (e.g., AWS, Azure, GCP) - Proficiency in programming languages such as Python, Java, or C++ - Strong software engineering skills with an emphasis on writing clean, modular, and maintainable code - Experience with version control systems (e.g., Git) and collaborative development workflows - Exceptional problem-solving and critical-thinking skills - Effective communication skills and ability to work in a collaborative team environment Preferred Qualifications - Bachelor's or advanced degree in Computer Science, Data Science, Machine Learning, or a related field - Experience with other database technologies (e.g., NoSQL, Graph) - Any of the below cloud certifications: - Google Cloud Professional Cloud Architect - Google Cloud Professional Database Engineer certification - Google Cloud Professional Data Engineer - Experience with additional data processing tools and technologies (e.g., Spark, Hadoop) - Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes) Salary Range $142,550 - $192,900

• Data ingestion for the Databricks environment. • Development and optimization of data pipelines using PySpark and SQL. • Integration with Microsoft Fabric for data management and governance. • Building semantic models in Power BI to support advanced analytics.

• Looking for a Senior Data Engineer with deep expertise in Microsoft Fabric and AI integration. • Hands-on experience architecting and delivering end-to-end data solutions within the Microsoft Fabric ecosystem, including Data Factory, Lakehouse, Warehouses, Notebooks, and Real-Time Intelligence. • Comfortable working with large-scale data pipelines, semantic models, and AI-powered workflows using Azure OpenAI or similar tools.

• Architect the platform. Set our warehouse/lakehouse direction and stand up the data lake and layered architecture that turns our raw system of record into trustworthy, queryable, intelligence-ready data. • Build the pipelines. Design and run batch and streaming pipelines that move data reliably out of our production systems - CDC, ELT, and real-time where it matters. • Model the data. Define the canonical datasets and models the whole company depends on, getting the grain, semantics, and contracts right. • Own reliability and accuracy. This is financial data, so correctness is non-negotiable. You'll own data quality, observability, integrity checks, and the testing and monitoring that let us trust it. • Build for a regulated environment. Design in role-based access, masking, lineage, and auditability from day one, and keep sensitive financial data out of places it doesn't belong. • Enable AI/ML and analytics. Build the feature pipelines and trustworthy data foundation our intelligence work relies on, moving us from systems of record toward systems of intelligence and action. • Set the standard. Establish the practices, tooling, and CI/CD for data that the future team inherits. You're setting the bar, not just clearing it.