Role Description We are looking for a Senior Data Engineer to help evolve and scale our modern data ecosystem, including our data lake, data warehouse, and machine-learning enablement platforms. This role will contribute to the company’s data-driven culture, bring innovative approaches to cloud-native engineering, and help advance our MLOps capabilities to support production-grade AI/ML initiatives. You will collaborate closely with data scientists, analytics engineers, and cross-functional partners to deliver reliable, high-quality data and operationalized machine-learning solutions. Responsibilities - Designs and builds scalable data extracts, integrations, transformations, and data models. - Ensures successful deployment and provisioning of data solutions across required environments. - Designs and implements data architectures and applications that enable speed, quality, and operational efficiency. - Interacts with cross-functional stakeholders to gather and define requirements and translate them into technical designs. - Develops deep familiarity with enterprise datasets, builds domain knowledge, and advances data quality. - Reviews requirements, identifies gaps, and drives resolution with stakeholders. - Identifies and recommends continuous improvement opportunities, ensuring integrations are automated, governed, and observable. - Serves as a key team member in designing and deploying a ground-up cloud data platform and pipeline. - Partners with data scientists to design, build, and maintain reproducible machine-learning pipelines, including feature engineering, model training, validation, deployment, and monitoring. - Implements CI/CD for data and ML workflows (model packaging, automated testing, environment management, release automation). - Builds and maintains production-grade ML infrastructure such as feature stores, model registries, data versioning, and experiment tracking frameworks (e.g., MLflow). - Ensures ML models follow best-practice governance, including automated model performance monitoring, drift detection, logging, observability, and alerting. - Designs scalable data pipelines optimized for ML workloads, such as batch, streaming, and real-time inference use cases. - Establishes MLOps standards, coding practices, and automation patterns that scale across teams. Qualifications - Bachelor or Master’s degree in technical discipline such as Computer Science, Information Systems or another technical field. - People person, team player with a strong can-do mentality. - 5+ years of experience as a Data Engineer within a data and analytics environment. - Strong interpersonal skills with a collaborative, proactive, and solution-driven mindset. - Proficiency in data modeling concepts and techniques. - Expertise with Databricks and other cloud data warehousing solutions such as S3, Redshift, or BigQuery. - Hands-on experience building data pipelines and ETL/ELT workflows using PySpark for semi-structured data (merge, delete, combine, wrangling). - Advanced knowledge of Python and advanced working SQL skills including query optimization. - Ability to write, test, and debug RESTful APIs. - Experience working in agile, cross-functional environments. - Strong analytical, problem-solving, and critical-thinking capabilities. - Ability to guide junior engineers and contribute to technical design reviews. - Strong communication skills with the ability to present complex concepts clearly. - Experience in data quality initiatives such as Master Data Management (MDM). - Experience operationalizing machine-learning models in production environments. - Hands-on experience with ML tooling such as MLflow, SageMaker, Databricks ML, Kubeflow, or similar. - Experience implementing CI/CD pipelines for data and ML workloads, including automated testing, deployment pipelines, and environment configuration. - Understanding of model lifecycle management, data versioning, feature store design, and model monitoring concepts. - Experience containerizing ML workloads using Docker and deploying them via cloud-native services or orchestrators. - Familiarity with monitoring frameworks, experiment tracking, and performance observability for ML models. Benefits - The expected salary range for this role is $116,000.00 - $170,000.00 USD. The actual base pay offered will be determined by factors such as the candidate's relevant experience, education, geographic location, and internal equity.