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Staff Data Engineer

We are looking for an experienced Staff Data Engineer to lead the development of our high-performance data pipelines and scalable APIs using Databricks and Rust. As a key technical leader, you will design, implement, and optimize data infrastructure, ensuring efficient data processing and real-time analytics capabilities. This role requires deep expertise in big data frameworks, distributed systems, and backend engineering, as well as a strong ability to mentor engineers and drive architectural decisions.

Essential Duties and Responsibilities

  • Design and Develop Data Pipelines: Design, build, and maintain scalable, high-performance data pipelines and ETL processes on Databricks/Microsoft Azure to acquire, ingest, process, and transform data from various sources into structured, usable formats.
  • Azure Data Platform Expertise: Leverage your in-depth knowledge of Microsoft Azure services such as Azure Databricks, Azure Storage, and Azure SQL Database to architect and optimize data solutions.
  • Architect and develop large-scale data pipelines in Databricks, leveraging Apache Spark (PySpark/Scala) for batch and streaming workloads.
  • Design, implement, and optimize high-performance Rust APIs to support data access, ingestion, and real-time processing.
  • Lead the development of scalable data infrastructure, ensuring reliability, security, and cost efficiency.
  • Integrate Databricks with cloud data lakes (Azure Data Lake, AWS S3, or GCP BigQuery) and relational/NoSQL databases such as MongoDB & Postgres.
  • Data Governance and Security: Implement data governance best practices and ensure data security, compliance and privacy measures are in place to protect sensitive data.
  • Optimize Spark jobs for efficiency and cost-effectiveness, implementing best practices for distributed computing.
  • Define and enforce data governance, quality, and security standards, including access control, encryption, and compliance.
  • Develop event-driven and real-time data processing pipelines using Kafka, Pulsar, or similar messaging systems.
  • Drive the adoption of best practices for Rust-based backend services, ensuring robust API security, logging, and monitoring.
  • Mentor and coach junior and mid-level engineers, providing guidance on system design, performance tuning, and scalable architecture.
  • Collaborate with cross-functional teams, including data scientists, software engineers, and DevOps, to enable seamless data-driven decision-making.
  • Lead and participate in technical discussions, driving architecture decisions and improving engineering standards.
  • Documentation and Training: Create technical documentation, best practices, and guidelines for data engineering processes, as well as provide training to team members.
  • Research and Innovation: Stay updated with the latest trends, tools, and technologies in the data engineering field and identify opportunities for innovation and continuous improvement.

The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

  • Minimum 5-7 years of experience in data engineering or related fields with experience on Azure data services and solutions.
  • Strong expertise with Databricks and Apache Spark (PySpark or Scala).
  • Proficiency in Rust for backend/API development
  • Proficiency in designing and implementing data solutions on the Microsoft Azure platform.
  • Experience with creating and deploying microservices (Docker, Kubernetes)
  • Strong programming skills in languages like Python, SQL, Scala, Rust.
  • Excellent verbal and written communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders.
  • Preferred Experience
  • Preferred to have expertise in Delta Lake, Apache Iceberg, or similar transactional data lakes.
  • Preferred to have knowledge of GraphQL and gRPC for efficient data API implementations.
  • Preferred to have experience working with machine learning data pipelines and feature stores.
  • Ability to analyze complex data requirements, troubleshoot issues, and propose innovative data engineering solutions.
  • Experience with medical image data such as X-ray, CT,MRI and familiarity with DICOM format is a plus
  • Professional-level certifications in Microsoft Azure, such as Azure Data Engineer Associate or Azure Solutions Architect Expert, are highly desirable.
  • Proven experience in leading and mentoring a team of junior data engineers or data interns.
  • Strong analytical skills are highly desirable.

Education and Experience

  • Bachelor’s degree in computer science, or a STEM related field. A master’s degree is a plus.

For roles based in the United States that require access to hospital facilities, must be eligible for and maintain credentials at all required hospitals, including meeting any applicable physical requirements or vaccination requirements (including the COVID-19 vaccine, as applicable).

ATEC is committed to providing equal employment opportunities to its employees and applicants without regard to race, color, religion, national origin, age, sex, sexual orientation, gender identity, gender expression, or any other protected status in accordance with all applicable federal, state or local laws. Further, ATEC will make reasonable accommodations that are necessary to comply with disability discrimination laws.

Salary Range

Alphatec Spine, Inc. complies with state and federal wage and hour laws and compensation depends upon candidate’s qualifications, education, skill set, years of experience, and internal equity. $140,000 to $160,000 Full-Time Annual Salary

Average salary estimate

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$160000K

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What You Should Know About Staff Data Engineer, Alphatec Spine

Join Alphatec Spine, Inc. as a Staff Data Engineer, where you'll be at the forefront of developing high-performance data pipelines and scalable APIs using Databricks and Rust. This position is integral to our mission, allowing you to leverage your expertise in architecting and optimizing data infrastructure that propels real-time analytics. With at least 5-7 years in data engineering, you’ll take charge of designing robust ETL processes on Databricks within the Azure ecosystem, ensuring that data is seamlessly transformed from numerous sources into structured formats. Your knowledge in Azure services, including Azure SQL Database and Azure Data Lake, will help you forge efficient, secure, and compliant data solutions. Your role also involves mentoring fellow engineers, optimally implementing Rust APIs and leading architectural decisions. Furthermore, you will work alongside cross-functional teams and stay current with trends to foster continuous improvement. If you’re passionate about data governance and leading innovation in the data engineering space, this role at Alphatec could be the perfect match for you!

Frequently Asked Questions (FAQs) for Staff Data Engineer Role at Alphatec Spine
What are the key responsibilities of a Staff Data Engineer at Alphatec Spine, Inc.?

The Staff Data Engineer at Alphatec Spine, Inc. is responsible for designing and maintaining scalable data pipelines, leveraging Azure Databricks, and developing efficient APIs in Rust. Your main tasks include architecting complex data solutions, ensuring data governance, and collaborating with data scientists and software engineers to enhance the decision-making process.

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What qualifications do I need to apply for the Staff Data Engineer position at Alphatec Spine, Inc.?

To apply for the Staff Data Engineer role at Alphatec Spine, Inc., you need a bachelor’s degree in computer science or a related STEM field, alongside 5-7 years of experience in data engineering. Strong expertise in Azure services, Databricks, Apache Spark, and proficiency in Rust are essential for this position.

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How does the Staff Data Engineer role at Alphatec Spine, Inc. support data security and governance?

As a Staff Data Engineer at Alphatec Spine, Inc., you will implement data governance best practices to ensure compliance and security. Your responsibilities entail defining quality standards, managing access control, and overseeing privacy measures to protect sensitive data in accordance with regulatory requirements.

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What technologies should a Staff Data Engineer at Alphatec Spine, Inc. be familiar with?

A Staff Data Engineer at Alphatec Spine, Inc. should be well-versed in Microsoft Azure services, Databricks, Apache Spark (PySpark/Scala), Rust for backend API development, and modern data processing tools such as Kafka or Pulsar. Familiarity with microservices (Docker, Kubernetes) and knowledge of Delta Lake would be advantageous.

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What growth opportunities are available for a Staff Data Engineer at Alphatec Spine, Inc.?

At Alphatec Spine, Inc., a Staff Data Engineer has numerous growth opportunities, including mentoring junior engineers, leading architectural discussions, and having the chance to innovate data engineering practices. With access to continued education and training, you can further enhance your skills and advance in your career.

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Common Interview Questions for Staff Data Engineer
Can you describe your experience with data pipelines and Azure Databricks?

In answering this question, outline specific projects where you designed and implemented data pipelines using Azure Databricks, emphasizing your contributions to scalability and performance. Be sure to discuss any optimization techniques you employed and how they improved data processing efficiency.

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What is your approach to ensuring data quality and governance?

Provide insights into your methodology for maintaining data quality and governance. Discuss how you implement data validation techniques, enforce quality checks, and define governance policies to protect sensitive data, using examples from your past roles to demonstrate your effectiveness.

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How have you used Rust in your previous projects?

When discussing your use of Rust, focus on specific projects where you developed APIs or backend services. Highlight Rust's performance benefits and how you ensured security and efficiency in data handling.

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What challenges have you faced while working with big data frameworks?

Identify a particular challenge you encountered with big data frameworks like Apache Spark. Explain how you approached the issue, what solutions you implemented, and the outcome of those solutions, emphasizing your analytical and problem-solving skills.

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How do you mentor junior data engineers?

Describe your mentoring philosophy and methods. Discuss specific instances where you've guided junior engineers, sharing successful outcomes and how your mentorship has contributed to the team's overall performance.

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What strategies do you employ for optimizing Spark jobs?

Explain the techniques you've used for optimizing Spark jobs, such as configuring partitions effectively, managing resources, and applying best practices for job execution. Share your experiences in achieving cost-effectiveness and improved job performance.

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Can you explain your experience with event-driven data processing?

Discuss relevant projects where you've implemented event-driven architectures, mentioning technologies like Kafka or Pulsar. Describe your approach to designing such systems and the challenges you addressed to ensure reliability and scalability.

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What tools do you use for monitoring and logging your data solutions?

Enumerate the monitoring and logging tools you prefer, such as Datadog or Prometheus, and explain how you utilize them to maintain the health of your data pipelines and APIs, assisting in real-time troubleshooting and performance analysis.

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How do you stay updated with the latest data engineering trends?

Share your strategies for continuous learning, like attending conferences, participating in online courses, or engaging in relevant communities. Highlight any recent trends you've explored that could benefit Alphatec Spine, Inc.'s data initiatives.

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Describe a time when you had to troubleshoot a complex data issue.

Recount a specific incident where you faced a complicated data problem. Detail your investigative steps, the collaboration involved, and the successful resolution, showcasing your critical thinking and technical expertise.

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DATE POSTED
February 28, 2025

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