Staff Data Engineer

Posted 9 Days Ago
Hiring Remotely in United States
Remote
Senior level
Sales • Software
Apollo is a leading B2B data intelligence and sales engagement platform, helping salespeople reach the right people.
The Role
Responsible for maintaining and operating data platforms, developing data pipelines, improving APIs, and ensuring data quality and monitoring.
Summary Generated by Built In

Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members.

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.
Why You’ll Love Working at Apollo

At Apollo, we’re driven by a shared mission: to help our customers unlock their full revenue potential. That’s why we take extreme ownership of our work, move with focus and urgency, and learn voraciously to stay ahead.

We invest deeply in your growth, ensuring you have the resources, support, and autonomy to own your role and make a real impact. Collaboration is at our core—we’re all for one, meaning you’ll have a team across departments ready to help you succeed. We encourage bold ideas and courageous action, giving you the freedom to experiment, take smart risks, and drive big wins.

If you’re looking for a place where your work matters, where you can push boundaries, and where your career can thrive—Apollo is the place for you.

Top Skills

Airflow
Fastapi
Hadoop
Kafka
Python
Spark
Spark Streaming
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The Company
HQ: San Francisco, California
190 Employees
On-site Workplace
Year Founded: 2015

What We Do

Founded in 2015, Apollo is a leading data intelligence and sales engagement platform trusted by 10,000 paying customers, from rapidly growing startups to some of the largest global enterprises. Its community-based approach to crowdsourcing data gives users maximum coverage while ensuring data accuracy.

Today, Apollo’s advanced algorithms and unique data acquisition methods help 1 million sales professionals enrich and analyze prospects’ data to increase quality conversations and opportunities.

Why Work With Us

Apollo is a product-led growth company focused as much on our customers as we are on our employees. Our culture is centered around two things: our values (ownership, integrity, curiosity, excellence, teamwork and fun), and our goal of helping customers maximize their full revenue potential on the Apollo platform.

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