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.
Implements CI/CD for data and ML workflows.
Builds and maintains production-grade ML infrastructure such as feature stores, model registries, and data versioning.
Ensures ML models follow best-practice governance, including automated model performance monitoring and alerting.
Designs scalable data pipelines optimized for ML workloads.
Establishes MLOps standards, coding practices, and automation patterns that scale across teams.

Requirements

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.
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.
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.
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.

Benefits

Employees (and their families) that meet eligibility criteria as outlined in applicable plan documents are eligible to participate in our medical, dental, vision, and basic life insurance programs and may enroll in our company’s 401(k) plan.
Employees will also be eligible for Time Off and Paid Sick Leave pursuant to the company’s policies.
Employees will enjoy paid holidays throughout the calendar year.
Hired applicant may be awarded Restrict Stock Units and receive annual bonuses pursuant to eligibility and performance criteria defined in the respective plan documents and policies.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

data extractiondata integrationdata transformationdata modelingETLELTPySparkPythonSQLRESTful APIs

Soft skills

analytical skillsproblem-solvingcritical thinkingcommunication skillscollaborationstakeholder engagementrequirements gatheringcontinuous improvementtechnical designpresentation skills

Certifications

Bachelor's degreeMaster's degree