Senior Data Engineer, Software Development Engineering

About the Role

Key Responsibilities

• Design and implement scalable, reliable, and maintainable data pipelines and architectures that ingest, transform, and store data from a variety of on-premises and cloud sources.
• Define and drive the organization's data strategy, including data modeling, governance, and infrastructure.
• Collaborate with software engineers, DevOps, and R&D teams to build systems that enable robust data collection, processing, and analysis.
• Develop tooling, frameworks, and libraries that accelerate data engineering workflows and promote reusability.
• Establish best practices and standards for data engineering within the organization (e.g., testing, documentation, deployment).
• Mentor and coach team members and stakeholders on data engineering concepts and practices.
• Identify opportunities for process automation, data quality improvements, and performance optimization.
• Evaluate and recommend appropriate technologies and platforms to support evolving data needs.

Requirements

• Strong proficiency in Python, SQL, and at least one modern data pipeline orchestration tool (e.g., Airflow, Prefect, Dagster).
• Deep experience designing and building data pipelines (ETL/ELT) and data processing frameworks.
• Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and their data-related services (e.g., S3, Redshift, BigQuery).
• Experience working in hybrid environments, integrating data from on-premise systems, IoT devices, robotics, lab sensors, etc., into cloud-based architectures.
• Knowledge of data modeling concepts (e.g., star/snowflake schema, normalized/denormalized models).
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Strong problem-solving skills and the ability to translate complex business needs into technical solutions.
• Experience with streaming data technologies (e.g., Kafka, Kinesis, Spark Structured Streaming).

Nice to Have

• Familiarity with Spark, dbt, Iceberg
• Familiarity with ML workflows
• Experience with CI/CD practices for data pipelines
• Exposure to DevOps practices, including infrastructure-as-code (e.g., Terraform, CloudFormation)
• Experience mentoring engineers or leading small teams
• Prior experience in R&D environments or working with highly cross-functional teams

Qualifications

• No specific formal educational qualifications specified in the description

Benefits & Perks

• Discretionary performance-based bonus
• Medical, vision, and dental coverage
• 401k
• Paid vacation, holidays, and sick time
• Other benefits