Data Platform

Data Platform Developer Jobs

1,398 jobs found

Today

Finance Data Engineer

Apple Austin, TX

Austin, TX

Today

$149k - $249k/yr Full time Artificial Intelligence Big Data Data Analytics

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Job Description

The Product Marketing Customer Analytics team is seeking a Finance Data Engineer to design and deliver advanced, scalable customer analytics data pipelines and platforms. The role focuses on building infrastructure optimized for analytics, data analysis, and data science, with an emphasis on data quality, performance, and collaboration with analytics consumers and data scientists.

Location

Austin, TX (onsite)

Compensation

Base pay range: USD 149,200 - 249,000 per year.

Role Summary

This position involves developing batch and streaming capabilities, improving reliability and runtime characteristics, and partnering with analytics stakeholders to address data engineering challenges at scale. The work also includes applying Generative AI techniques, including Retrieval Augmented Generation (RAG), to enhance analytics capabilities.

Responsibilities

Develop and automate large-scale, high-performance, scalable data platform capabilities for batch and/or streaming to support faster analytics.
Design large-scale, complex applications and frameworks with strong runtime characteristics, including low latency, fault tolerance, and high availability.
Build and maintain custom frameworks to support engineering and analytics needs.
Collaborate with analytic consumers and data scientists to improve new and existing constructs and solve data engineering problems at scale.
Build data pipelines using Spark, Trino, lakehouse architectures, or similar distributed platforms and Snowflake.
Implement inclusive data quality checks to help ensure high-quality data.
Promote strong software engineering practices for building data infrastructure and pipelines at scale.
Apply structured thinking to break down ambiguous problems and propose impactful solutions.
Use Generative AI and Retrieval Augmented Generation (RAG) techniques to enhance data analytics capabilities.
Maintain strong documentation and technical writing, with effective verbal and written communication.
Bring attention to detail in execution and communication.

Required Qualifications

5+ years of relevant Data Engineering experience.
Undergraduate degree in Computer Science, MIS, Engineering, Mathematics, or another quantitative discipline.
5+ years of experience in data engineering and ETL pipeline development.
5+ years of experience with Big Data Technologies including Spark, Lakehouse, and Trino.
Experience with Kubernetes and Docker is preferred.
Familiarity with Retrieval Augmented Generation (RAG) techniques working with LLMs.
Experience creating and consuming Model Context Protocol (MCP) services.
Experience with Snowflake.

Technologies

Spark, Trino, Lakehouse, Snowflake, Kubernetes, Docker, Generative AI, Retrieval Augmented Generation (RAG), LLMs, Model Context Protocol (MCP).

Benefits

Comprehensive medical and dental coverage.
Retirement benefits.
Discounted products and free services.
Reimbursement for certain educational expenses, including tuition.
Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs.
Eligibility for discretionary restricted stock unit awards.
Ability to purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan.
Discretionary bonuses or commission payments and relocation (if eligible).

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Today

Data Engineer

Ledgent Technology Irvine, CA

Irvine, CA

Today

$131k - $150k/yr Full time Analytics Automation Developer Business Analytics

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Job Description

Join Ledgent Technology as a Data Engineer based in Irvine, CA, working onsite 3 days per week (local to Southern California required). This role supports a modern data platform and gives you the chance to keep production-ready systems running while improving how teams move and manage data across ETL, Data Lake, and MDM.

What You’ll Work On

In this position, you will be responsible for keeping the group’s data environment in operational condition, with hands-on involvement across key components such as ETL, Data Lake, and MDM. You’ll also work with the tools and technologies used to connect, transform, and make data accessible for downstream usage.

Maintain the Group’s data environment in operational condition, including ETL, Data Lake, and MDM

Required Skills

This role requires a strong foundation in data engineering with a focus on Snowflake and experience spanning multiple data and analytics tools. You should also be comfortable working with both core data platforms and the engineering workflows that support them.

Must have Snowflake
5+ years of development experience with Snowflake and Talend Cloud
5+ years of development experience with data visualization tools such as Tableau or Power BI
5+ years of expert-level SQL skills
Experience with Openflow and Python
Local to Southern California; 3x days onsite in Irvine
AI is not required, but you must want to learn it as you go

Tools and Technologies

You’ll work across a stack that includes Snowflake, Talend Cloud, SQL, and analytics and reporting tools such as Tableau and Power BI. The role also involves Openflow and Python as part of day-to-day data engineering work.

Compensation: USD 131,000 - 150,000 per year.

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Today

Software Engineer II - AI Performance

Disney Entertainment and ESPN Product & Technology New York, NY

New York, NY

Today

$125k - $167k/yr Full time Backend Developer APIs Artificial Intelligence

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Job Description

Disney Entertainment and ESPN Product & Technology is hiring a Software Engineer II to strengthen reliability for operational AI systems. On the Reliability Insights team, you will design and build data and reporting pipelines that turn telemetry, AI agent interactions, and operational logs into measurable results for real-world performance, precision, and business impact.

This onsite role in New York, NY focuses on bridging backend engineering with data-driven evaluation. You will connect AI signals to incident lifecycles, quantify reliability outcomes, and deliver dashboards and metrics that help leadership and technical teams act on model health over time.

What you will do

Design, build, and maintain automated feedback pipelines that capture operational outcomes (confirmed incidents, false positives, dismissed alerts) and correlate them directly to AI model predictions and agent actions.
Create programmatic evaluation frameworks to track model precision, recall, and drift over time, including automated threshold alerts to trigger model tuning and reduce alert fatigue.
Build end-to-end data pipelines that link AI outputs to incident lifecycles, measuring business impact using metrics such as Service to Incident correlation, notification success rates, and Mean Time to Mitigation (MTTM).
Design and deploy scalable APIs, metrics layers, and single-pane model health dashboards that communicate AI ROI, operational health, and performance narratives to both leadership and technical stakeholders.
Implement mechanisms to capture structured end user feedback and feed real-world usage signals back into ongoing model improvement cycles.

Qualifications

3+ years of applicable experience in backend and data pipeline engineering, including building scalable APIs (for example, FastAPI, Flask) and metrics or insight generation platforms.
Strong data platform engineering experience with distributed processing tools such as PySpark, SQL, Pandas, Databricks, and Snowflake for telemetry processing and analysis.
Practical knowledge of model evaluation concepts including precision, recall, model drift, and ground-truth labeling systems.
Hands-on experience with modern development workflows: GitHub (version control), Docker (containerization), and cloud-native deployments on AWS and EKS.
Proficiency with AI-assisted development tools such as Cursor and Claude Code to increase engineering velocity.
Strong analytical skills to translate complex system telemetry and operational logs into reliability metrics and business insights.
Excellent cross-functional collaboration and communication, including partnership with operational teams such as SRE and DevOps to turn raw data into actionable insights.

Technologies

FastAPI, Flask, PySpark, SQL, Pandas, Databricks, Snowflake
GitHub, Docker, AWS, EKS
Cursor, Claude Code

Compensation

The hiring range for this position in New York, NY is $124,900.00 to $167,400.00 per year.
Base pay may vary based on internal equity and the candidate’s geographic region, job-related knowledge, skills, and experience.
A bonus and/or long-term incentive units may be included in the compensation package, along with medical, financial, and/or other benefits, depending on the level and position offered.

Education: Bachelor’s degree in Computer Science, Engineering, or equivalent experience.

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Today

ROOTS Hub ETL Developer

Vaya Health Charlotte, NC 🇺🇸 Remote

Charlotte, NC

Today

$93k - $120k/yr Full time Backend Developer Azure Azure Data Factory

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Job Description

Vaya Health’s ROOTS Hub ETL Developer role supports RHTP (Rural Health Transformation Program) by building the data foundation that helps teams analyze information tied to strategic initiatives and ongoing business needs. This position is a remote, home-based role operating Monday through Friday from 8:30am to 5:00pm EST, with work that spans requirements gathering, ETL/ELT solution design and delivery, and operational support in a healthcare and PHI-aware environment.

What you’ll do

Design, develop, test, and implement ETL/ELT solutions that enable analysis aligned to strategic initiatives and current business requirements.
Build Azure Data Factory and Microsoft Fabric pipelines, integrating Dataverse and source system data.
Create Lakehouse-based ingestion and transformation patterns and support curated datasets used for analytics and reporting.
Analyze requirements, design solutions, and implement end-user requested data capabilities.
Contribute to defining business requirements, producing specifications for end-user sign-off before development.
Design pipeline architecture including data mappings, transformation logic, validation rules, scheduling, monitoring, and deployment approaches across Azure, Dataverse, Microsoft Fabric, Data Factory, Lakehouse, and related enterprise data platforms.
Coordinate User Acceptance Testing, confirm end-user satisfaction, and deliver the final solution.
Follow change control procedures and practices, and produce basic technical documentation.
Monitor the health, performance, reliability, and data quality of supported ETL/ELT processes, including Azure Data Factory and Microsoft Fabric pipelines, Lakehouse loads, and Dataverse integrations.
Identify patterns that may indicate service issues across separate solutions, document remediation steps, operational procedures, source-to-target mappings, and support processes.
Develop and execute security standards applicable to integration development in line with adopted policy.
Apply industry standards and best practices to protect information, communications, and data throughout solution development.

Core qualifications

5+ years of hands-on experience designing, developing, and supporting ETL/ELT solutions using Azure Data Factory (ADF), Microsoft Fabric Data Factory, Microsoft Fabric Lakehouse, and SQL Server Integration Services (SSIS), including ingestion, transformation, orchestration, scheduling, monitoring, error handling, and performance optimization.
Experience integrating Microsoft Dataverse with enterprise applications, databases, APIs, and external sources across cloud and on-premises environments.
Extensive experience with Microsoft SQL Server, including database development, data integration, performance tuning, and data warehousing solutions.
Expert-level SQL and T-SQL skills, including complex queries, stored procedures, functions, views, data modeling, and query optimization.
Strong knowledge of data engineering best practices, including metadata-driven processing, incremental loading, data quality management, security, resiliency, documentation, version control, and CI/CD automation.
Experience with source control and DevOps tools, including Git and automated deployment frameworks.
Proficiency with Microsoft Visual Studio and SQL Server Management Studio (SSMS) and related development tools.
Experience with Microsoft Power Apps and other Power Platform technologies.
Experience with data warehousing, dimensional modeling, and modern cloud-based analytics platforms.
Ability to gather, analyze, and document business and technical requirements, data mappings, specifications, and solution designs.
Strong analytical and troubleshooting skills to diagnose and resolve complex data, integration, and performance issues.
Experience working in Agile/Scrum environments and managing multiple priorities in a fast-paced, deadline-driven setting.
Excellent verbal and written communication skills for technical and non-technical stakeholders.
Experience handling and protecting sensitive data, including Protected Health Information (PHI), in alignment with regulatory and organizational requirements.
Strong proficiency with Microsoft 365, including Teams, SharePoint, and Office applications.
A High School Diploma is required; a Bachelor’s degree in specified fields is preferred.
Microsoft certification in Azure, Fabric, SQL Server, Data Engineering, or related technologies is not required, but preferred.

Technologies

Azure Data Factory (ADF), Microsoft Fabric Data Factory, Microsoft Fabric Lakehouse, SQL Server Integration Services (SSIS)
Microsoft Dataverse, SQL Server, SQL, T-SQL
Microsoft Visual Studio, SQL Server Management Studio (SSMS)
Microsoft Power Apps, Power Platform
Git, Microsoft 365 (Teams, SharePoint, Office applications)
Agile/Scrum, CI/CD

Location and scheduling

Remote (home-based, virtual position) with Monday–Friday hours from 8:30am–5:00pm EST.
Vaya Partners welcomes applications from candidates living in NC, SC, GA, TN, VA, MD, or FL.
Residency requirement: the person in this position must live in NC, SC, GA, TN, VA, MD, or FL.

Additional notes

This role may require access to confidential healthcare information, including PHI, and requires training in applicable laws (including HIPAA and 42 CFR Part 2) as well as observing those laws as an employee of Vaya Partners.
The individual must sign a confidentiality statement as an employee of Vaya Partners.

Compensation and application

Salary range: USD 92,558 - 120,326 per year.
Depending on qualifications and experience of the candidate.
This position is Exempt and eligible for overtime compensation.
Application deadline: open until filled.
Apply via Vaya Partners Career Center: https://www.vayahealth.com/about/careers/.

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Today

Lead Data Engineer

Simpalm Dallas, TX

Dallas, TX

Today

$103k - $126k/yr Full time Manager Artificial Intelligence Data

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Job Description

Lead enterprise-scale, AI-ready data solutions for Simpalm’s client as an on-site Lead Data Engineer in Dallas, TX.

Responsibilities

Lead analysis, design, development, and implementation of scalable data solutions across the enterprise data warehouse and modern data platform
Collaborate with Product Engineers, Data Engineers, Architects, Data Scientists, and AI Engineers to turn business requirements into data and AI solutions
Design, build, and optimize ETL/ELT pipelines using Snowflake, Nexla, SQL, and Python
Develop and manage Apache Airflow DAGs for scheduling, reliability, and operational observability
Design AI-ready data architectures for analytics, machine learning, GenAI, RAG, vector databases, and agentic AI solutions
Use AI-assisted development tools (Microsoft Copilot, Claude, Cursor) to accelerate delivery and improve code quality and documentation
Define ingestion, transformation, governance, and serving patterns for AI training and inference workloads
Support metadata management, data lineage, data observability, and governance practices for enterprise AI initiatives
Evaluate and recommend emerging AI technologies, frameworks, and best practices to strengthen engineering capability
Manage agile sprint execution: prioritize work, support backlog refinement, estimate stories, track dependencies, and meet delivery commitments
Coordinate across onsite, offsite, offshore, and cross-functional teams to unblock delivery and maintain momentum
Create and maintain technical design docs, data flow diagrams, AI solution architecture diagrams, and operational runbooks
Drive data quality, performance tuning, monitoring, and production support for critical pipelines and Snowflake workloads
Lead architecture reviews and code reviews, and facilitate root-cause analysis and problem-solving sessions
Mentor team members in modern data engineering and AI-assisted development practices
Promote responsible AI practices covering data privacy, security, governance, and ethical use of AI

Requirements

Bachelor’s degree in computer science, IT, engineering, data science, AI, or a related field, or equivalent experience
Strong hands-on Snowflake experience: SQL development, performance tuning, data modeling, stored procedures, and scalable processing patterns
Hands-on ETL/ELT pipeline experience with Nexla and modern data integration tools
Experience with Apache Airflow (or similar): DAG development, scheduling, monitoring, and troubleshooting
Strong proficiency in SQL and Python for enterprise-scale data engineering
Practical experience with AI-powered development tools including Microsoft Copilot, Claude, Cursor, and GitHub Copilot (or similar)
Working knowledge of GenAI concepts: LLMs, RAG, vector embeddings, prompt engineering, and agentic AI patterns
Experience designing data architectures and pipelines supporting AI, ML, analytics, and intelligent automation
Knowledge of data governance, security, privacy, lineage, and compliance for AI and enterprise data platforms
Proven ability to lead sprint execution, manage competing priorities, clarify scope, identify risks, and coordinate delivery
Strong collaboration across product engineering, data engineering, AI/ML, BI/analytics, QA, architecture, and business teams
Experience working in an Agile environment with onsite, offsite, offshore, and distributed teams
Strong analytical, problem-solving, communication, and troubleshooting skills
Self-starter with ownership, accountability, and attention to detail in a fast-paced environment
Demonstrated ability to learn and adopt emerging AI technologies in a rapidly evolving landscape

Technologies

Snowflake, Nexla, SQL, Python
Apache Airflow, Apache Airflow DAGs
Microsoft Copilot, Claude, Cursor, GitHub Copilot
LLMs, Retrieval-Augmented Generation (RAG), vector databases
vector embeddings

Preferred Qualifications

Experience with Snowflake on Azure and related Azure data services
Experience with Azure OpenAI, Microsoft Fabric, Databricks, Amazon Bedrock, Anthropic Claude, OpenAI, or similar AI platforms
Experience implementing RAG, semantic search, vector databases, or agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel)
Experience building AI-enabled data products, intelligent document processing, conversational AI, or enterprise knowledge retrieval systems
Experience with additional ETL/ELT tools: Azure Data Factory, Talend, and Python-based frameworks
Experience with DevOps, CI/CD, Infrastructure-as-Code, and MLOps supporting AI/data platform deployments
Experience with Jira, Azure DevOps, GitHub, or similar Agile delivery and tracking tools
Experience with metadata management, data quality frameworks, or Master Data Management (MDM) initiatives
Experience in insurance, insurtech, healthcare, or financial services
Relevant certifications in Snowflake, Azure, Generative AI, Claude, Microsoft Copilot, AI engineering, or cloud data platforms

Location and Salary

Location: Dallas, TX (onsite)
Salary: USD 103,000 - 126,000 per year

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Today

Machine Learning Engineer

Spectrix Analytical Services, LLC Remote 🌍 Remote

Remote

Today

$48 - $58/hr Contract Artificial Intelligence Aws Ml Cloud Machine Learning

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Job Description

This contract, remote role supports the development and operation of an ML Platform and agentic AI toolchain for protein therapeutic design. The work covers compute infrastructure, MLOps, and agentic capabilities, with end-to-end connections from data and models into downstream analytics in Snowflake.

Key Responsibilities

Develop and maintain production-grade Python services, data pipelines, and command-line tooling.
Build and maintain Helm charts, container images, and the deployment infrastructure used as part of daily delivery.
Deploy and operate Kubernetes workloads across multiple AWS regions and accounts.
Instrument pipelines and models so performance, costs, and benchmark outcomes are measurable.
Collaborate with scientists and engineering leads to determine what to build and help teams adopt new capabilities through documentation and hands-on support.

Required Qualifications

Education and experience aligned to the role: Bachelor’s degree with 7–10 years of relevant experience, or Master’s degree with 5+ years of relevant experience.
Strong Python development skills, with experience building production tooling or production pipelines.
Working knowledge of Kubernetes, Helm, and container-based deployments.
Experience developing, deploying, or optimizing machine learning pipelines in production.
Familiarity with infrastructure-as-code and CI/CD practices.
Experience with AWS, including multi-region or multi-account deployments.
Experience with data warehousing and ELT patterns, with Snowflake preferred.
Experience working on a small engineering team (approximately 3–10 engineers) with end-to-end system ownership and independent prioritization, without dedicated QA, SRE, or release functions.
Routine use of agentic coding harnesses such as Claude Code, OpenCode, Codex, or an equivalent tool as part of day-to-day development, including an understanding of where they help and where they do not.
Willingness to learn computational biology and protein science domain knowledge.
Ability to communicate effectively while working independently in a remote setting and collaborating with both engineers and scientists.

Primary Work Areas

Extend a Kubernetes orchestration platform and the workflows that run on it, including migrating existing application backends onto Kubernetes.
Make the platform region-agnostic so compute scheduling can expand to new AWS regions and accounts through configuration rather than code changes.
Implement model registration, versioning, and serving so model outputs are available to scientific applications and downstream reporting.
Integrate model outputs with the data layer to avoid leaving results as per-project artifacts.
Build automated benchmarking and retraining for computational tools, LLM models, and agent skills used in de novo design, including GPU-backed benchmarking of larger models.
Track test sets and datasets and maintain a persistent record of benchmark results over time.
Incorporate agentic AI into existing scientific applications by defining tools and skills, building evaluation harnesses, and providing safe plumbing for agents to call internal services.
Deliver reliable S3-to-Snowflake data pulls and automated registration of de novo design results, with monitoring and backfill behavior that does not require manual intervention.
Optimize existing production pipelines to reduce runtime and cost through profiling, GPU utilization improvements, batching, scheduling, and container image updates.

Tech Stack

Python
Kubernetes
Helm
Containerized deployments
AWS
S3
Snowflake
CI/CD
Infrastructure-as-code
Claude Code
OpenCode
Codex

Compensation and Employment Details

Job type: Contract
Location: Remote
Compensation: USD 48–58 per hour

Benefits

Automatic benefits for life insurance and short and long term disability insurance
Optional benefits for full time employees: health, dental, and vision insurance
College loan assistance
Employer matched retirement savings plan

Company Overview

Spectrix Analytical Services, LLC provides on-site analytical chemistry services and has delivered reliable, high-quality results since 1999. The team of experienced scientists supports R&D efforts across multiple industries by generating precise analytical data and maintaining sophisticated instrumentation at clients’ laboratories.

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Today

AI Data Engineer

UKG (Ultimate Kronos Group) Lowell, MA

Lowell, MA

Today

$72k - $103k/yr Full time Artificial Intelligence Data Data Analysis

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Job Description

Build and support data pipelines, analytics, and AI-enabled capabilities in a hybrid entry-level role.

Responsibilities

Develop and maintain data ingestion and transformation pipelines
Write SQL and Python to process, validate, and prepare data
Organize data into trusted, reusable datasets for reporting and analytics
Assist with data quality checks, issue investigation, and pipeline monitoring
Support development and testing of AI and machine learning solutions
Work with cloud services and tools for data storage, processing, and model development
Document data flows, code, and technical processes
Collaborate with data engineers, architects, analysts, and business partners
Follow established standards for data security, governance, and software development

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience
Foundational experience with SQL and Python via coursework, projects, internships, or work experience
Understanding of databases, data structures, and basic data processing concepts
Interest in cloud data platforms and artificial intelligence
Strong problem-solving, communication, and teamwork skills
Willingness to learn new tools and technologies