[Hiring] Senior Software Engineer, Data Acquisition @People Data Labs

Role Description

You will be crucial in accelerating our efforts to build standalone data products that enable data teams and independent developers to create innovative solutions at massive scale. In this role, you will be working with a team to continuously improve our existing datasets as well as pursuing new ones.

If you are looking to be part of a team discovering the next frontier of data-as-a-service (DaaS) with a high level of autonomy and opportunity for direct contributions, this might be the role for you. We like our engineers to be thoughtful, quirky, and willing to fearlessly try new things. Failure is embraced at PDL as long as we continue to learn and grow from it.

What You Get to Do

• Contribute to the architecture and improvement of our data acquisition and processing platform, increasing reliability, throughput, and observability
• Use and develop web crawling technologies to capture and catalog data on the internet
• Build, operate, and evolve large-scale distributed systems that collect, process, and deliver data from across the web
• Design and develop backend services that manage distributed job orchestration, data pipelines, and large-scale asynchronous workloads
• Structure and model captured data, ensuring high quality and consistency across datasets
• Continuously improve the speed, scalability, and fault-tolerance of our ingestion systems
• Partner with data product and engineering teams to design and implement new data products powered by the data you help collect, and enhance and improve upon existing products
• Learn and apply domain-specific knowledge in web crawling and data acquisition, with mentorship from experienced teammates and access to existing systems

Qualifications

• 7+ years of professional experience building or operating backend or infrastructure systems at scale
• Solid programming experience in Python, Go, Rust, or similar, including experience with async / await, coroutines, or concurrency frameworks
• Strong grasp of software architecture and backend fundamentals; you can reason clearly about concurrency, scalability, and fault tolerance
• Solid understanding of browser rendering pipeline, web application architecture (auth, cookies, http request / response)
• Familiarity with network architecture and debugging (HTTP, DNS, proxies, packet capture and analysis)
• Solid understanding of distributed systems concepts: parallelism, asynchronous programming, backpressure, and message-driven design
• Experience designing or maintaining resilient data ingestion, API integration, or ETL systems
• Proficiency with Linux / Unix command-line tools and system resource management
• Familiarity with message queues, orchestration, and distributed task systems (Kafka, SQS, Airflow, etc.)
• Experience evaluating and monitoring data quality, ensuring consistency, completeness, and reliability across releases

Requirements

• Work independently in a fast-paced, remote-first environment, proactively unblocking themselves and collaborating asynchronously
• Communicate clearly and thoughtfully in writing (Slack, docs, design proposals)
• Write and maintain technical design documents, including pipeline design, schema design, and data flow diagrams
• Scope and break down complex projects into deliverable milestones, and communicate progress, risks, and blockers effectively
• Balance pragmatism with craftsmanship, shipping reliable systems while continuously improving them

Some Nice To Haves

• Degree in a quantitative field such as computer science, mathematics, or engineering
• Experience as a Red Teamer
• Experience working on large-scale data ingestion, crawling, or indexing systems
• Experience with Apache Spark, Databricks, or other distributed data platforms
• Experience with streaming data systems (Kafka, Pub/Sub, Spark Streaming, etc.)
• Proficiency with SQL and data warehousing (Snowflake, Redshift, BigQuery, or similar)
• Experience with cloud platforms (AWS preferred, GCP or Azure also great)
• Understanding of modern data storage and design patterns (parquet, Delta Lake, partitioning, incremental updates)
• Knowledge of modern data design and storage patterns (e.g., incremental updating, partitioning and segmentation, rebuilds and backfills)
• Experience building and maintaining data pipelines on modern big-data or cloud platforms (Databricks, Spark, or equivalent)

Benefits

• Stock
• Competitive Salaries
• Unlimited paid time off
• Medical, dental, & vision insurance
• Health, fitness, and office stipends
• The permanent ability to work wherever and however you want
• Comp: $160K - $200K