178 applicants

Company

Arity · 19 hours ago

Senior Data Engineer - Arity

United States

Full-time

Remote

Mid, Senior Level

$86K/yr - $153K/yr

5+ years exp

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Information ServicesInformation Technology

Work & Life Balance

No H1B

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Responsibilities

Design, build, and maintain end-to-end data and machine learning pipelines to support analytics, reporting, and AI-driven applications.

Develop and optimize scalable ETL/ELT processes to extract, transform, and load data from diverse sources into a cloud-based platform.

Architect and manage data storage solutions within the data platform (e.g., data lakes, warehouses, and marts) to enable advanced analytics and machine learning.

Implement and manage ML pipelines, building feature pipelines and deploying models.

Collaborate with data scientists, analysts, and software engineering to integrate data products into business workflows.

Ensure data quality, consistency, and governance by implementing robust monitoring, validation, and alerting mechanisms.

Lead the adoption of new cloud-native technologies to streamline and enhance data and ML operations.

Mentor junior data engineers, fostering a culture of innovation and knowledge-sharing within the team.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Data EngineeringMachine LearningCloud IntegrationPythonAWSGCPETL/ELT ProcessesData Processing FrameworksData Orchestration ToolsScalaJavaTensorFlowPyTorchScikit-learnApache SparkFlinkBeamKafkaKinesisData LakesData WarehousesData MartsAirflowPrefectDbtDockerKubernetesGeospatial DataBI ToolsMLOps

Required

Bachelor’s degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or a related field. A Master’s degree is preferred.

5+ years of professional experience in data engineering, including end-to-end pipeline development and cloud integration.

Proven experience with machine learning workflows, including data preparation, feature engineering, and model deployment.

Proficiency in programming languages such as Python, Scala, or Java, with an emphasis on ML libraries like TensorFlow, PyTorch, or Scikit-learn.

Strong knowledge of data processing frameworks (e.g., Apache Spark, Flink, Beam) and real-time data streaming (e.g., Kafka, Kinesis).

Hands-on expertise with AWS or GCP ecosystems, including tools like: AWS: SageMaker, Redshift, Glue, Athena, EMR; GCP: BigQuery, Vertex AI, Dataflow, Dataproc.

Solid understanding of relational and non-relational database systems (SQL, NoSQL).

Experience with data orchestration tools (e.g., Airflow, Prefect, dbt) and CI/CD practices.

Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes) is a plus.

Preferred

Experience with geospatial data like trajectories.

Experience deploying machine learning models into production environments with monitoring and optimization strategies.

Familiarity with cloud security and compliance best practices for data and ML workflows.

Proficiency in BI tools (e.g., Tableau, Looker, Power BI) and data visualization.

Strong understanding of MLOps practices and tools for automated ML lifecycle management.

Relevant certifications in cloud platforms (e.g., AWS Data Engineer, Machine Learning Engineer; Google Cloud Professional ML Engineer, Professional Data Engineer) are a plus.