[Hiring] Senior Data Engineer @Velir

Role Description

Senior Data Engineers play a crucial role in helping our client organizations manage and leverage their data effectively. Their responsibilities include:

• Data Architecture Design
• Data Warehousing
• Data Quality Assurance
• Scalability and Performance Optimization
• Data Security

As senior-level individual contributors, SDEs are also responsible for recommending and implementing data engineering tools and technologies that best suit the client’s needs. Because our clients are mostly US-based organizations, we look for the ability to communicate with professional proficiency in English, verbally and in writing.

Responsibilities

• Data Engineering Leadership: Responsible for bridging technical gaps to lead and oversee various data engineering projects for our data solutions engagements.
• Cross-Team Collaboration: Contributing to alignment across the organization through influence and by translating context across different teams.
• Project Enablement: Ensuring that large and/or more complex engineering projects are delivered in alignment with the appropriate business outcomes.

Tools & Technologies

• Programming languages (e.g. SQL, Python)
• Data Processing (e.g. Apache Spark, dbt)
• Cloud-based data warehouses (e.g., Snowflake, Google BigQuery)
• Data orchestration (e.g., Apache Airflow, Azure Data Factory, Prefect)

Technical Skills

• Data Movement: Optimizes data pipelines to achieve low latency, ensuring timely processing and delivery of data.
• Data Warehousing: Acts as an internal resource for data warehousing, providing help with advanced techniques and strategies for novel scenarios.
• Programming: Has an authoritative or deep holistic understanding, dealing with routine matters intuitively.
• Technical Management: Able to be a technical leader across a set of related team's domains.
• Data Infrastructure: Can design, build, and maintain the underlying data infrastructure for a complex, large-scale system.

Bonus Points For

• Data Modeling and Transformation: Comfortable with modeling and transforming extremely large and complex datasets.
• MLOps: Understands and can execute the steps required to deploy a trained model into a production environment.

Skills & Qualifications

• Proven experience as a Data Engineer or related role, with a focus on designing and developing data pipelines.
• Strong programming skills in Python and SQL. Experience with Scala and Rust is a plus but not required.
• Deep knowledge of data warehousing and ETL/ELT processes.
• Intermediate / expert proficiency with common data integration / orchestration platforms (e.g., Fivetran, Azure Data Factory, Apache Airflow).
• Deep experience with at least one cloud data warehouse (Snowflake, BigQuery, Databricks, or similar).
• Experience with streaming solutions such as Spark Streaming, Kafka, or Flink is desirable but not required.
• Strong experience with at least one cloud platform such as AWS, Azure, or Google Cloud.
• Familiarity with machine learning operations (MLOps) techniques and platforms is a plus but not required.
• Experience mentoring and advising other engineers.
• Ability to define, explain, and sell long-term vision for technical architecture, technical approaches / procedures, etc.
• Excellent communication and collaboration skills.

Physical Requirements

• Frequent sitting at a desk performing work on a computer.
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Compensation Range

$145,000 - 160,000 annually. Please note that compensation packages are finalized after the interview process is concluded.

Core Company Values

• Take the Long View - Ensure the company is built to last.
• Be Courageous - Make the right decisions even when they aren't the easiest decisions.
• Be Genuine - Bring honesty and authenticity to all that you do.
• Work with Focus + Passion - Display purpose and pride in your work and never stop learning.