About the Role
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.
- 5+ years of experience as a Data Engineer within a data and analytics environment.
- 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.
- Ability to guide junior engineers and contribute to technical design reviews.
- 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.
Highly Desired AWS certifications (any):
- DevOps experience with CICD & unit/integration testing, Docker containerization, workflow orchestration.
- Databricks certifications – Associate/Professional.
- AWS Certified Solutions Architect – Associate/Professional.
- AWS Certified Developer – Associate/Professional.
- AWS Certified DevOps Engineer.
- AWS Certified Solutions Architect.
- AWS Certified Data Analytics.
- AWS Certified Security - Specialty.
- AWS Certified Cloud Practitioner.