Staff Data Engineer
As a Staff Data Engineer, you will be responsible for maintaining and operating the data platform that caters to machine learning workflows, analytics and powers some of the products offered to Apollo customers.
Daily adventures/responsibilities
Develop and maintain scalable data pipelines and build new integrations to support continuing increases in data volume and complexity.
Develop and improve Data APIs used in machine learning / AI product offerings
Implement automated monitoring, alerting, self-healing (restartable/graceful failures) features while building the consumption pipelines.
Implement processes and systems to monitor data quality, ensuring production data is always accurate and available.
Write unit/integration tests, contribute to the engineering wiki, and document work.
Define company data models and write jobs to populate data models in our data warehouse.
Work closely with all business units and engineering teams to develop a strategy for long-term data platform architecture.
Competencies
Customer driven: Attentive to our internal customers’ needs and strive to deliver a seamless and delightful customer experience in data processing, analytics, and visualization.
High impact: Understand what the most important customer metrics are and make the data platform and datasets an enabler for other teams to achieve improvement.
Ownership: Take ownership of team-level projects/platforms from start to finish, ensure high-quality implementation, and move fast to find the most efficient ways to iterate.
Team mentorship and sharing: Share knowledge and best practices with the engineering team to help up-level the team.
Agility: Organized and able to effectively plan and break down large projects into smaller tasks that are easier to estimate and deliver. Can lead fast iterations.
Speak and act courageously: Not afraid to fail, challenge the status quo, or speak up for a contrarian view.
Focus and move with urgency: Prioritize for impact and move quickly to deliver experiments and features that create customer value.
Intelligence: Learns quickly, demonstrates the ability to understand and absorb new codebases, frameworks, and technologies efficiently.
Qualifications
Required:
8+ years of experience as a data platform engineer or a software engineer in data or big data engineer.
Experience in data modeling, data warehousing, APIs, and building data pipelines.
Deep knowledge of databases and data warehousing with an ability to collaborate cross-functionally.
Bachelor's degree in a quantitative field (Physical/Computer Science, Engineering, Mathematics, or Statistics).
Preferred:
Experience using the Python data stack.
Experience deploying and managing data pipelines in the cloud.
Experience working with technologies like Airflow, Hadoop, FastAPI and Spark.
Understanding of streaming technologies like Kafka and Spark Streaming.
Staff Data Engineer
As a Staff Data Engineer, you will be responsible for maintaining and operating the data platform that caters to machine learning workflows, analytics and powers some of the products offered to Apollo customers.
Daily adventures/responsibilities
Develop and maintain scalable data pipelines and build new integrations to support continuing increases in data volume and complexity.
Develop and improve Data APIs used in machine learning / AI product offerings
Implement automated monitoring, alerting, self-healing (restartable/graceful failures) features while building the consumption pipelines.
Implement processes and systems to monitor data quality, ensuring production data is always accurate and available.
Write unit/integration tests, contribute to the engineering wiki, and document work.
Define company data models and write jobs to populate data models in our data warehouse.
Work closely with all business units and engineering teams to develop a strategy for long-term data platform architecture.
Competencies
Customer driven: Attentive to our internal customers’ needs and strive to deliver a seamless and delightful customer experience in data processing, analytics, and visualization.
High impact: Understand what the most important customer metrics are and make the data platform and datasets an enabler for other teams to achieve improvement.
Ownership: Take ownership of team-level projects/platforms from start to finish, ensure high-quality implementation, and move fast to find the most efficient ways to iterate.
Team mentorship and sharing: Share knowledge and best practices with the engineering team to help up-level the team.
Agility: Organized and able to effectively plan and break down large projects into smaller tasks that are easier to estimate and deliver. Can lead fast iterations.
Speak and act courageously: Not afraid to fail, challenge the status quo, or speak up for a contrarian view.
Focus and move with urgency: Prioritize for impact and move quickly to deliver experiments and features that create customer value.
Intelligence: Learns quickly, demonstrates the ability to understand and absorb new codebases, frameworks, and technologies efficiently.
Qualifications
Required:
8+ years of experience as a data platform engineer or a software engineer in data or big data engineer.
Experience in data modeling, data warehousing, APIs, and building data pipelines.
Deep knowledge of databases and data warehousing with an ability to collaborate cross-functionally.
Bachelor's degree in a quantitative field (Physical/Computer Science, Engineering, Mathematics, or Statistics).
Preferred:
Experience using the Python data stack.
Experience deploying and managing data pipelines in the cloud.
Experience working with technologies like Airflow, Hadoop, FastAPI and Spark.
Understanding of streaming technologies like Kafka and Spark Streaming.