Google Cloud Dataflow

Google Cloud Dataflow Engineer Jobs

3 jobs found

Today

Staff Software Engineer, Data Platform (FedRAMP)

Okta Bellevue, WA

Bellevue, WA

Today

$194k - $267k/yr Full time Backend Developer AWS Big Data

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Job Description

Okta is hiring a Staff Software Engineer to help build and operate a FedRAMP-aligned data platform, focused on streaming infrastructure and foundational services for analytics and ML.

Responsibilities

Design, implement, and take ownership of data-intensive, high-performance, scalable platform components
Partner with engineering teams, architects, and cross-functional partners on project development, design, and implementation
Participate in design reviews and code reviews, and perform analysis and performance tuning
Coach and mentor engineers to support team growth and engineering scale-up
Debug production incidents across services and multiple layers of the stack

Requirements

8+ years of experience in object-oriented languages, preferably Java
Hands-on experience with cloud-based distributed computing technologies, including messaging systems such as Kinesis and Kafka
Hands-on experience with data processing systems such as Flink, Spark, and Beam
Hands-on experience with storage and compute systems such as Snowflake and Hadoop
Hands-on experience with coordinators and schedulers such as those used in Kubernetes, Hadoop, and Mesos
Experience developing and tuning highly scalable distributed systems
Excellent grasp of software engineering principles
Solid understanding of multithreading, garbage collection, and memory management
Reliability engineering experience, including data quality, data observability, and incident management

Technologies

Java, Kinesis, Kafka, Flink, Spark, Beam
Snowflake, Hadoop, Kubernetes, Mesos, ElasticSearch

About the Team

Data Platform team builds foundational data services, systems, and data products for Okta that support user outcomes
Streaming analytics
Interactive end-user reporting
Data and ML platform to scale Okta capabilities
Telemetry for products and data

About the Position

Scale high volume, low latency distributed data-platform services and data products
Build foundational infrastructure to support Okta for years to come
Design, build, and deploy systems powering data analytics and ML
Work across an analytics infrastructure stack using modern technologies including Kinesis, Flink, ElasticSearch, and Snowflake

Nice to Have

Experience maintaining security, encryption, identity management, or authentication infrastructure
Hands-on experience leveraging major public cloud providers to build mission-critical, high volume services
Hands-on experience developing data integration applications for large scale (petabyte scale) environments across batch and online systems
Contributed to distributed systems at high volume or criticality, such as Kafka or Hadoop
Experience developing Kubernetes-based services on the AWS Stack

Federal Environment Requirements

Ability to access federal environments and/or have access to protected federal data
As a condition of employment, documentation establishing U.S. Person status (e.g., U.S. Citizen, National, Lawful Permanent Resident, Refugee, or Asylee per 22 CFR 120.15) must be submitted upon hire

Location: Bellevue, WA (hybrid)
Salary: USD 194,000 - 267,000 per year
Minimum Experience: 8+ years

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2d ago

Google Cloud AI Engineer

Altimetrik Corp Remote 🌍 Remote

Remote

2d ago

Full time Artificial Intelligence Big Data Bigquery

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Job Description

Altimetrik Corp is seeking a Google Cloud AI Engineer to design and deploy AI agents grounded in unique business data as part of Google’s Data Cloud for the agentic era. This role blends agentic design with AI-on-data strategy, with a strong focus on real-time inference, secure data grounding, and turning high-level business goals into robust technical architectures.

What you’ll be doing

Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows.
Use the Agent Developer Kit (ADK) to build and manage multi-agent systems that collaborate to address end-to-end business challenges.
Integrate tools such as MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases including BigQuery and Spanner.
Use Vertex AI for model training, tuning, and deployment, ensuring smooth integration with BigQuery for feature engineering.
Build and optimize streaming data pipelines (for example, via Dataflow) to support real-time inference using RunInference API or Vertex AI endpoints.
Ground models in live business context by leveraging vector engines within BigQuery or AlloyDB to reduce “AI amnesia”.
Attend internal and client-facing meetings promptly, and deliver regular, structured status updates on milestones and technical blockers.
Request help when facing technical hurdles and contribute to a collaborative troubleshooting environment.

What you bring

Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation.
Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques including scaling, encoding, and imputation.
Hands-on experience with Google Cloud Storage and Vertex AI endpoints.
Familiarity with stateful real-time processing and recent innovations in agentic architectures.

Helpful background

Experience in financial services or retail to support industry-specific data logic (for example, credit risk, royalty forecasting, or search relevance).
Knowledge of privacy and compliance practices for handling PII using masking and redaction.

Benefits

401(k)
Health insurance

Location and travel

Location: Remote

Travel: 25% (Required)

Application check

Willingness to complete a pre-employment background check contingent upon an offer.

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4d ago

GCP BigQuery Data Engineer

Virtues Irving, TX

Irving, TX

4d ago

$100k - $110k/yr Full time Analytics Big Data Bigdata

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Job Description

Build cloud-native, scalable analytics and near-real-time ingestion pipelines across GCP BigQuery and event-driven streaming systems.

Responsibilities

Design, develop, and optimize enterprise data warehouse solutions using Google BigQuery.
Design, develop, and maintain real-time data ingestion pipelines using Apache NiFi to capture, transform, and route streaming data into Apache Kafka topics.
Build event-driven streaming architectures across Apache NiFi, Apache Kafka, and Google BigQuery for high-throughput, fault-tolerant, low-latency processing.
Configure and optimize Apache NiFi processors for extraction, transformation, routing, filtering, schema validation, error handling, retries, and reliable delivery to Kafka.
Develop streaming ingestion solutions enabling Google BigQuery to consume Kafka event streams and transform data into analytical tables and enterprise reporting datasets.
Design data models and implement ETL/ELT processes to move data from raw to curated and published layers.
Create and manage large-scale BigQuery datasets, including temporary/permanent and internal/external tables.
Optimize BigQuery workloads through query tuning, partitioning, clustering, and cost optimization techniques.
Monitor, troubleshoot, and optimize streaming workloads by tuning NiFi flows, Kafka topics/partitions, and BigQuery streaming ingestion to support high availability, data integrity, minimal latency, and cost-efficient processing.
Build scalable data lake frameworks and ingestion pipelines using GCP technologies.
Develop reporting and visualization solutions using Looker, Looker Studio (Data Studio), Connected Sheets, and other BigQuery reporting tools.
Collaborate with data architects, modelers, developers, DevOps engineers, project managers, and business stakeholders to deliver scalable enterprise analytics and continuous data platform improvements.

Requirements

7+ years of experience in Data Engineering, Data Warehousing, or Big Data platforms.
Must-have skills: Apache NiFi and Apache Kafka.
Strong hands-on experience with Google Cloud Platform (GCP), including BigQuery, Cloud Dataflow, Pub/Sub, and Google Cloud Storage (GCS).
Hands-on experience designing and supporting real-time streaming pipelines using Apache NiFi, Apache Kafka, and Google BigQuery.
Experience using BigQuery Console/Query Editor for data management, performance tuning, and SQL development.
Strong experience designing scalable data models, ETL/ELT pipelines, data lake architectures, and enterprise data warehouse solutions.
Experience with batch and streaming ingestion using GCP services.
Thorough understanding of BigQuery cost structure (storage, ingestion, and query costs) and experience implementing query optimization, partitioning, clustering, and other cost optimization techniques.
Experience managing large-scale datasets, including temporary/permanent and internal/external BigQuery tables.
Experience developing reporting and visualization solutions using Looker, Looker Studio (Data Studio), and Connected Sheets.
Excellent verbal and written communication skills, able to collaborate effectively with technical teams, business stakeholders, senior management, and executive leadership.
Experience working in Agile environments and collaborating with cross-functional teams to deliver enterprise data engineering solutions.

Technologies

Google BigQuery
Apache NiFi
Apache Kafka
Google Cloud Dataflow
Google Pub/Sub
SQL
ETL/ELT
Google Cloud Storage (GCS)
Looker
Looker Studio (Data Studio)
Connected Sheets
DevOps
CI/CD
Agile/Scrum
Python
Spark
Data Modeling
Data Warehousing
Data Lakes
Batch & Streaming Pipelines

Preferred Qualifications

Google Cloud certifications.
Experience delivering enterprise-scale analytics and cloud data solutions.

Pay

$100,000.00 - $110,000.00 per year

Work Location

Onsite in Irving, TX

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