Sr. Data Engineer I

Job 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.

Highly Desired AWS certifications (any):

• DevOps experience with CICD &