Senior Data Engineer

Plaid

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
New York, NYSeattleSan Francisco, CA
Salary
$190,800–$238,800 / yr
Posted
8 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $175k
This role $215k
$122k most similar roles pay here $251k

This role pays more than 82% of similar roles. Most pay $140,262–$209,675 — the shaded band above. At the midpoint, this role pays about $215k versus about $175k for comparable roles.

Based on 240 similar postings.

Employer

About Plaid

Plaid is a financial technology company that builds a data network powering digital finance applications, enabling consumers to securely connect their financial accounts to apps and services. Industry: Financial Technology & Data Infrastructure

Plaid currently has 35 open roles on FindRole.

Listed pay typically runs $190,800–$262,800 across 35 roles with salary data.

Most-posted roles

View all roles at Plaid

At a glance

TL;DR · Senior Data Engineer

As a Senior Data Engineer, you will join the Data Platform team to scale data systems while maintaining high standards for accuracy and completeness. You will be responsible for building and owning core SQL and Python data pipelines that power the company's data lake and warehouse. Your daily work involves designing golden datasets, managing infrastructure, and creating visualizations to enable business leaders to make informed decisions. To achieve these goals, you will utilize a technical stack including DBT, Airflow, Redshift, Atlan, Retool, Spark, Kafka, and Golang applications. You will also manage large-scale data models ranging from 500TB to petabytes across various warehouses like Snowflake and Databricks. This role focuses on solving complex data pipeline issues at scale while ensuring data quality, performance, and privacy for the company's internal analytics and reporting needs.

What does a Data Engineer earn in California?

Median $199800 from 35 postings across 19 companies.

See salary data

What you'll do

  • Own core SQL and Python data pipelines that power the company's data lake and warehouse.
  • Design and maintain high-quality datasets with defined uptime, performance standards, and clear documentation.
  • Build and manage batch and real-time data pipelines using technologies like Spark and Kafka.
  • Develop analytics schemas from unstructured data to support internal business intelligence.
  • Lead large-scale data engineering projects that require cross-functional collaboration across product, engineering, and marketing teams.
  • Manage and improve low-level data infrastructure to ensure system reliability and scalability.
  • Create proof-of-concepts for new technologies to balance technical advancement with user adoption.
  • Advocate for industry best practices and tools to improve the company's overall data strategy.

What we're looking for

  • You must have 4+ years of dedicated data engineering experience solving complex pipeline issues at scale.
  • You must have experience building data models and pipelines on top of large datasets ranging from 500TB to petabytes.
  • You must be proficient in SQL and comfortable with modern orchestration tools like DBT, Mode, and Airflow.
  • You must have experience working with performant warehouses and data lakes such as Redshift, Snowflake, or Databricks.
  • You must have experience building and maintaining batch and real-time pipelines using technologies like Spark and Kafka.
  • You must be able to design and evolve analytics schemas on top of unstructured data.
  • You should be excited to try new technologies and produce proof-of-concepts that balance technical advancement with user experience (preferred).

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