Design and implement scalable, reliable, and efficient data infrastructure solutions to support the organization's data processing, storage, and analytics needs
Collaborate with data engineers, software engineers, and data scientists to understand data requirements and translate them into technical specifications and architecture designs
Develop and maintain data pipelines for ingesting, processing, and transforming large volumes of data from various sources, ensuring data quality and integrity
Optimize data storage and retrieval processes, including database schema design, indexing strategies, and query optimization, to enhance performance and reduce latency
Implement monitoring, alerting, and logging mechanisms to proactively identify and troubleshoot issues in the data infrastructure, ensuring high availability and reliability
Stay updated on emerging technologies, tools, and best practices in data engineering and infrastructure management, and recommend adoption as appropriate
Work closely with stakeholders to understand business requirements and priorities
Requirements
Bachelor’s or Master’s degree in computer science, engineering, business analytics, data science, or related fields and experience
12+ years of overall experience in implementing and managing data infrastructure and platforms
7+ years of experience in data & analytics leadership role in managing cross functional teams that creates, engineers, builds and maintains data infrastructure and platforms
5+ years of strong experience in data engineering or infrastructure roles, with a focus on designing and building scalable modern data platforms (data lakes, lake houses etc.) with a deep understanding of cloud-base data platforms
Proficiency in programming languages such as Python, Java, or Scala, and experience with data processing frameworks such as Apache Spark, Snowflake, Apache Flink, or Hadoop
Strong understanding of distributed systems, cloud computing platforms (e.g., AWS, GCP, Azure), and containerization technologies (e.g., Docker, Kubernetes)
Deep experience with relational databases and data warehousing technologies (e.g., PostgreSQL, MySQL, Redshift, Snowflake) and NoSQL databases (e.g., Cassandra, MongoDB)
Hands-on experience with data pipeline orchestration tools such as Apache Airflow, Luigi, or Prefect
Excellent problem-solving skills and the ability to troubleshoot complex issues in a distributed environment
Strong communication skills and the ability to collaborate effectively
Experience with Agile methodologies and DevOps practices is a plus
Proven ability to manage and lead team of data engineers, data ops engineers
Benefits
Flexibility to work where/how you want within your country of employment – in-office, remote, or hybrid
Continued investment in your professional development
Day 1 access to a robust health and wellness benefits package, including an annual wellness stipend
401k with up to a 4% match and immediate vesting
Flexible and generous (FTO) time-off
Employee Stock Purchase Program
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data infrastructuredata pipelinesdatabase schema designquery optimizationdata engineeringcloud-based data platformsprogramming languages (Python, Java, Scala)data processing frameworks (Apache Spark, Snowflake, Apache Flink, Hadoop)relational databases (PostgreSQL, MySQL, Redshift, Snowflake)NoSQL databases (Cassandra, MongoDB)