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5d ago

Data Engineer

Major League Baseball (MLB) New York, NY

New York, NY

5d ago

$115k - $140k/yr Full time Python Analytics Apache Airflow

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Job Description

Join MLB's LAI team as a hands-on data engineer shaping production pipelines with Airflow and dbt on GCP to power analytics across clubs and the Commissioner's Office.

Responsibilities

Build production-grade pipelines using Airflow and dbt to orchestrate batch and streaming transformations across GCP, so downstream analysts and engineers can trust the data they query without checking the wiring.
Architect clean, layered data models (staging, intermediate, mart) that serve as the single source of truth for league analytics, applying dbt best practices for materialization, testing, and documentation.
Operate the ingestion layer using Pub/Sub, GCS, Dataflow, and Knowledge Catalog DataPlex to land both batch and streaming sources cleanly into the lakehouse.
Implement observability and monitoring standards so that data quality issues surface before stakeholders notice them.
Manage code through GitHub-based CI/CD, contributing to deployment workflows that keep our platform reliable and our changes safe.
Adhere to data governance practices that keep proprietary baseball data secure and compliant.

Requirements

2–4 years of production data engineering experience.
Expert-level SQL — comfortable writing complex freehand queries (sub-queries, nested logic, window functions) and reading someone else’s to spot issues.
Strong Python for data processing, scripting, and automation.
Hands-on dbt experience — built models across staging, intermediate, and mart layers, written tests, and shipped to production.
Production Airflow experience — DAG authoring, dependency management, debugging failed runs.
Deep familiarity with Google Cloud Platform (BigQuery, GCS, Pub/Sub) or equivalent depth in AWS/Azure with willingness to convert.
Git-based development workflows — branches, PRs, code reviews as a daily practice.
Clear communication with both engineers and non-engineers, receptive to feedback and able to give it kindly.
Execution mindset. You can own a project from requirements to deployment with minimal oversight.

Technologies

Airflow
dbt
Python
SQL
GitHub
BigQuery
GCS
Pub/Sub
Dataflow
Knowledge Catalog DataPlex
Terraform

Benefits

Competitive Benefits Package
Company Contributed 401K Plan
Paid Time Off and Holidays
Paid Parental Leave
Access to Free Tickets to Baseball Games & MLB.TV
Discounts at MLB Store | MLBShop.com
Employee Assistance Programs (EAP)
Onsite/Online Training & Development Programs
Tuition Reimbursement
Disability Benefits (short term and long term)
Life and Accidental Death Insurance
Pet Insurance

Salary

Base salary: USD 115,000 - 140,000 per year, plus bonus.

Nice to have

A degree in Computer Science, Engineering, or a related field — or non-traditional background with equivalent practical experience.
Experience with Terraform or other Infrastructure-as-Code tools.
Experience with AI-assisted development or enterprise AI tooling (Gemini Enterprise, Vertex AI).
A passion for baseball, or prior experience in sports, media, or entertainment.
Ability to build creative solutions for unusual problems.

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9d ago

Data Engineer

Crumbl Provo, UT

Provo, UT

9d ago

Full time Cloud Cloud Platform Data Architecture

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Job Description

Join Crumbl's data team at the Provo headquarters to help turn raw data into actionable insights. This on site Data Engineer role focuses on building and maintaining scalable data pipelines that power data driven decisions across the organization, using dbt and Prefect to orchestrate data workflows. You will partner with data scientists, analysts, and other stakeholders to ensure data quality and accessibility.

Responsibilities

Design, build, and maintain scalable and reliable data pipelines through ELT and ETL.
Collaborate with data scientists, analysts, and stakeholders to capture data requirements and uphold data quality.
Develop and maintain documentation, including data dictionaries, workflow diagrams, and data flow diagrams.
Safeguard data integrity and security by implementing appropriate controls and monitoring.
Tune data pipelines to optimize processing efficiency and query performance.
Implement and maintain data security policies and procedures, including access controls, encryption, and data masking.
Design and implement data processing workflows using dbt and Prefect to support data science and machine learning applications.
Develop and maintain data ingestion processes to bring data from external sources into the organization’s data environment.
Identify and address performance issues with pipelines and collaborate with infrastructure and operations teams to optimize system performance.
Test and validate data pipelines to ensure they are functioning correctly and meeting business requirements.
Participate in code reviews and contribute to the development of data engineering best practices.
Stay current with emerging technologies and trends in data engineering and data science, identifying opportunities to leverage them within the organization.

Requirements

Bachelor’s or Master’s degree in Data Science, Information Systems, or a related field.
3+ years building and maintaining production data pipelines (degree in a related field or equivalent experience).
Advanced SQL with window functions, CTEs, and performance tuning on large datasets.
Strong Python for data engineering, emphasizing modular, testable pipeline code.
Hands-on dbt experience, including models, tests, macros, and incremental materializations.
Production Snowflake experience: schema design, performance tuning, and warehouse/cost optimization.
AWS data services experience (S3, Glue, Lambda).
Data quality and observability with dbt plus Elementary.
Infrastructure as code with Terraform and version control with Git.
Dimensional data modeling (star/snowflake schemas, SCDs) and lakehouse concepts.
Strong problem solving and clear communication with analysts, scientists, and stakeholders.

Technologies

dbt
Prefect
SQL
Python
Snowflake
AWS S3
AWS Glue
AWS Lambda
Terraform
Git
Elementary

Benefits

Medical, dental, and vision benefits
15 days PTO per year
10 paid holidays
Paid parental leave
Personal phone bill reimbursement
Gym reimbursement
Corporate DoorDash DashPass membership
Regular company and team activities
401k with competitive matching contribution plan
Excellent opportunities for career growth
Work in a hyper-growth company

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