Data Engineer

Pika - Palo Alto HQ - original posting ->
Status
Open
Remote policy
Onsite
Employment type
Full-time
Salary
Not stated
Categories
Engineering
Tech
awsgcpbigquerykafkapostgresredissnowflakeairflowdockerkubernetesgopythononsitedata
Source
pika
First observed
2026-09-22 22:36 UTC
Last seen
2026-09-22 22:36 UTC
Source claims posted
2026-09-22 21:53 UTC
Consecutive misses
0 of 3

What the posting says

About Pika

At Pika, we’re building the next generation of AI creative tools to empower human creativity. Our mission is to make video creation seamless, intuitive, and accessible to everyone, leveraging the power of advanced AI. We believe that AI should amplify creative expression—enabling everyone to create, collaborate, and communicate across media. Our team includes engineers, artists, and product thinkers, all passionate about building tools that unlock new creative possibilities.

Pika has raised significant funding and is backed by leading investors, with a collaborative culture based in Palo Alto, CA. We prefer hybrid in-office, sharing ideas and launching products together.

About the Role

We are seeking a Data Engineer to design, build, and scale the data infrastructure powering Pika’s creative AI platform. As a Data Engineer, you will play a key role in architecting, implementing, and maintaining our data pipelines and analytics systems, enabling our team to make data-driven decisions and deliver world-class AI experiences. You will work closely with product, engineering, and data teams to ensure data is accurate, reliable, and accessible for users and internal business needs.

You will combine software engineering know-how with data architecture expertise, helping us build robust, scalable, and high-performance systems. Your contributions will directly support the success of millions of creators and help shape the future of AI-powered media tools.

What You’ll Do

Design, develop, and maintain scalable data pipelines and ETL workflows

Build, automate, and optimize our data infrastructure for analytics, reporting, and machine learning applications

Ensure data quality, consistency, and security across all sources and sinks

Collaborate with engineering, analytics, and product teams to define data requirements and deliver reliable datasets

Implement monitoring solutions and proactively resolve data pipeline issues

Optimize storage and data processing performance for growth and efficiency

Contribute to data modeling efforts and schema design for analytics and product needs

Help establish best practices and empower a data-driven culture across the organization

What We’re Looking For

4+ years of experience as a data engineer or in a similar role designing, building, and maintaining data infrastructure

Strong software engineering background with proficiency in Python, SQL, and/or similar languages

Hands-on experience with data pipeline orchestration tools (Airflow, Prefect, Dagster, etc.)

Experience with cloud data platforms (AWS/GCP, Redshift, BigQuery, Snowflake, etc.)

Knowledge of database systems, data modeling, and data warehousing best practices

Familiarity with monitoring, logging, and data quality practices for data workflows

Excellent analytical and problem-solving skills with attention to detail

Great communication skills and ability to work cross-functionally in a collaborative environment

Self-motivated, curious, and comfortable in a fast-paced, high-growth startup

Nice to Have

Experience supporting data for machine learning or AI-powered applications

Familiarity with real-time or streaming data architectures (Kafka, Kinesis, etc.)

Prior work at high-growth startups or experience with rapid scaling

Open source, hackathon, or data engineering community experience

Our Stack

Python, Go, Node.js, Postgres, Redis, Docker, Kubernetes, AWS/GCP

What We Offer

Competitive salary in the AI industry

Substantial equity in a fast-growing startup defining the future of AI and creativity

Comprehensive health benefits, monthly stipends, and company retreats

Collaborative, high-growth culture—everyone contributes to growth and success

Quality

Completeness: 65%

Not enough history yet to judge honesty signals.

Timeline

  1. *
    #932096 2026-09-22 22:36 UTC
    Published