Senior Data Engineer

People Data Labs (PDL) is the provider of people and company data. We do the heavy lifting of data collection and standardization so our customers can focus on building and scaling innovative, compliant data solutions. Our sole focus is on building the best data available by integrating thousands of compliantly sourced datasets into a single, developer-friendly source of truth. Leading companies across the world use PDL’s workforce data to enrich recruiting platforms, power AI models, create custom audiences, and more.

We are looking for individuals who can balance extreme ownership with a “one-team, one-dream” mindset. Our customers are trying to solve complex problems, and we only help them achieve their goals as a team. Our Data Engineering Team is the secret sauce behind all that we do and we are looking for the best of the best.

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.

Build infrastructure for ingestion, transformation, and loading an exponentially increasing volume of data from a variety of sources using Spark, SQL, AWS, and Databricks

Building an organic entity resolution framework capable of correctly merging hundreds of billions of individual entities into a number of clean, consumable datasets.

Developing CI/CD pipelines and anomaly detection systems capable of continuously improving the quality of data we're pushing into production.

Dreaming up solutions to largely undefined data engineering and data science problems.

5-7+ years of industry experience with clear examples of strategic technical problem-solving and implementation

Strong software development fundamentals.

Experience with Python

Expertise with Apache Spark (Java, Scala, and/or Python-based)

Experience with SQL

Experience building scalable data processing systems (e.g., cleaning, transformation) from the ground up.

Experience using developer-oriented data pipeline and workflow orchestration (e.g., Airflow (preferred), dbt, dagster or similar)

Knowledge of modern data design and storage patterns (e.g., incremental updating, partitioning and segmentation, rebuilds and backfills)

Experience working in Databricks (including delta live tables, data lakehouse patterns, etc.)

Experience with cloud computing services (AWS (preferred), GCP, Azure or similar)

Experience with data warehousing (e.g., Databricks, Snowflake, Redshift, BigQuery, or similar)

Understanding of modern data storage formats and tools (e.g., parquet, ORC, Avro, Delta Lake)

Balance high ownership and autonomy with a strong ability to collaborate

Work effectively remotely (able to be proactive about managing blockers, proactive on reaching out and asking questions, and participating in team activities)

Demonstrate strong written communication skills on Slack/Chat and in documents

Exhibt experience in writing data design docs (pipeline design, dataflow, schema design)

Scope and breakdown projects, communicate and collaborate progress and blockers effectively with your manager, team, and stakeholders

Degree in a quantitative discipline such as computer science, mathematics, statistics, or engineering

Experience working with entity data (entity resolution / record linkage)

Experience working with data acquisition / data integration

Expertise with Python and the Python data stack (e.g., numpy, pandas)

Experience with streaming platforms (e.g., Kafka)

Experience evaluating data quality and maintaining consistently high data standards across new feature releases (e.g., consistency, accuracy, validity, completeness)

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