Sr. Data Engineer I

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.

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.

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.

Strong interpersonal skills with a collaborative, proactive, and solution-driven mindset.

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.

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.

DevOps experience with CICD & unit/integration testing, Docker containerization, workflow orchestration