Jobs Data Engineer

We are looking for a Middle Data Analytics Engineer to build reusable, production-grade pipelines that power our data deals, which are large-scale curated training sets for leading AI labs. You’ll ship engineering solutions with immediate revenue impact and collaborate with some of the world’s largest AI companies. 

 

Responsibilities:

• Work with the business development (BD) team, content experts, data science, and engineering teams to understand customer requirements and available metadata.
• Map customer requirements to our existing metadata and apply data modelling practices to maximize reusability and scalability
• Build DBT models that are capable of querying up to 1B rows efficiently
• Refine DBT models based on visual QA to ensure accurate datasets at scale.
• Propose and implement automated solutions that streamline the curation process and increase scalability.
• Serve as the engineering point of contact, partnering with Business Development to ensure accurate timelines, clear requirements, and manage all technical implementation pieces of the workflow
• Assist the expansion of the data curation workflow to accommodate data science approaches within DBT, streamline the review process to ensure a more rigorous statistical approach with more comprehensive testing

Requirements:

• Strong SQL background with an understanding of query optimization and Snowflake experience.
• DBT experience preferred. 
• Great communication skills.
• 3-5 years of experience in analytics/data engineering.
• Perform code reviews and collaborate with the team to create an optimized ecosystem.

Nice to have:

• BigQuery.
• Python.
• ClickHouse.
• Experience delivering projects in an operational environment. 
• Interest in data science to aid the understanding of customer use cases.
• Interest in photography, music and video creation.

ABOUT THE ROLE

Due to continuous growth, our Data Platform is excited to welcome a motivated Data Engineer to help us align reliable marketing, payments, and product data with business goals to drive organizational success.
As a Data Engineer, you’ll play a vital role in building and operating the pipelines that feed daily reporting, attribution, and LTV and be responsible for ingesting data from ad platforms, payment
systems, and product backends into Databricks, orchestrating jobs in Airflow, and keeping silver/gold tables accurate and on time. Working closely with Analytic, Marketing and Data Science teams, you’ll have the opportunity to make a meaningful impact on both our products
and processes.
If you’re passionate about solving complex problems, collaborating across departments, and driving results, this role is perfect for you!

YOUR MISSION
As a Data Engineer you will:
● Proactively identify, troubleshoot, and resolve issues to optimize both internal andexternal processes.
● Balance multiple projects without sacrificing quality, aligning your work with brand standards, cultural nuances, and regulatory requirements.
● Engage with diverse stakeholders (e.g., product, engineering, and marketing teams) to gather requirements, evaluate options, and develop tools and processes to support our organization’s goals.

WHAT WILL MAKE YOU STAND OUT FROM THE CROWD
We’re looking for someone with a balance of hard and soft skills who can thrive in a dynamic,
cross-functional environment. Here’s what we’d love to see:
● Excellent analytical and problem-solving abilities, with a strategic approach to tackling challenges.
● Strong collaboration skills for working seamlessly with cross-functional teams.
● Exceptional communication skills for engaging stakeholders across various levels.
● Advanced English proficiency in both speaking and writing, enabling clear and effective interactions.
● Experience with Python, SQL, Spark, Databricks (Unity Catalog / Delta), Apache Airflow, and GCP (GCS, BigQuery); dbt and ads/payments APIs (Google Ads, Stripe, RevenueCat or similar) are a plus. A solid understanding of agile methodologies.
● Comfort owning production: scheduled jobs, data quality checks, alerting, and getting a failed daily run back on time.
● Curiosity about the business behind the tables — marketing spend, subscriptions, and attribution — not only the pipelines that move them.

ZentixSoft is looking for a Senior Data Architect! 🚀

Format: Full-Time Outstaffing, long-term remote engagement. You will take full end-to-end ownership of target-state data architecture across multi-cloud environments.
 

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Responsibilities:

• Architecture Ownership: Define and own the target-state data architecture across both AWS and Azure cloud environments.
• Platform & Processing Design: Architect enterprise data lakes/warehouses, storage layers, ingestion patterns, and processing frameworks utilizing distributed systems (Spark).
• Governance & Security: Establish enterprise data governance, security, compliance, and access-control standards across both cloud ecosystems.
• Tool & Vendor Evaluation: Evaluate tools/services across AWS and Azure, balancing performance, scalability, maintainability, and cloud costs.
• Technical Leadership & Delivery: Provide architectural oversight and detailed technical documentation to engineering leads during implementation, supporting cloud migration and multi-cloud integration initiatives.

🎓 Requirements:

• Architect Experience: 8+ years in data architecture and engineering, with clear senior/lead-level design responsibility and platform ownership.
• AWS Stack: Deep hands-on architecture experience with AWS (S3, Redshift, Glue, Lake Formation, Athena).
• Azure Stack: Deep hands-on architecture experience with Azure (ADLS, Synapse, Data Factory, Databricks).
• Data Engineering Core: Mastery of data modeling, ETL/ELT pipelines, distributed data processing (Spark), and enterprise data lakes/warehouses.
• Governance & Migrations: Proven track record leading multi-cloud integrations, cloud migrations, and establishing data security and governance standards.
• Communication: Excellent stakeholder communication skills to present architectural decisions to both technical teams and business leadership.
   

➕ Nice to Have:

• Cloud Certifications (AWS Certified Solutions Architect, Azure Solutions Architect Expert).
• Experience with Data Mesh or Domain-Driven Data Architecture.
• Experience in regulated industries (Finance, Healthcare, etc.).
   

📍 Project Details:

⏳ Duration: Long-term (Full-time).

📍 Location: Fully Remote.

Position Description:
As a Data Engineer, you will play a key role in designing and maintaining scalable data pipelines and platforms.
You’ll work closely with data scientists, analysts, and product teams to ensure high-quality, reliable, and scalable data is always available. This role requires deep technical expertise, strong analytical skills, and exposure to emerging AI/LLM technologies
Location(s): Germany & EU
Working Model: remote

Role Overview
As a Senior Data Engineer, you will play a key role in designing and maintaining scalable data pipelines and platforms. You’ll work closely with
data scientists, analysts, and product teams to ensure high-quality, reliable, and scalable data is always available. This role requires deep
technical expertise, strong analytical skills, and exposure to emerging AI/LLM technologies.

Key Responsibilities

• Design, develop, and optimize scalable data pipelines using Python and PySpark.
• Build, manage, and optimize big data solutions leveraging Google Cloud Platform (BigQuery, DataProc, Dataflow).
• Collaborate with cross-functional teams to translate business requirements into efficient data models and workflows.
• Ensure data accuracy, consistency, and availability across multiple systems and applications.
• Implement best practices for data governance, quality, and security.
• Stay updated with advancements in big data, cloud, and AI technologies, recommending innovative solutions.
• Mentor junior engineers and establish best practices within the team.

Requirements for "Professional Skills"
All professional skills required to fulfill the following general task description:
Works as a developer and is responsible for the development and programming of software, tools, systems, etc., as well as the associated troubleshooting and error analysis. This role requires comprehensive knowledge of various programming languages as well as appropriate development tools, methodologies, and development processes.

Works on the integration of systems, applications, or databases with one another or into other systems/software. This role requires exceptional knowledge of scripting and various programming languages, along with relevant integration tools, methods, and processes. It also requires a strong understanding of the technologies or systems being integrated.

Also works as an engineer and documents operational requirements in the form of system performance parameters and system configurations. He/she is additionally responsible for ensuring the seamless integration of architecture and development.

This also includes system integration or network integration activities, for example, SDM.

Projects using Agile development methodology:
If the contractor works within a Scrum team, they possess extensive knowledge of Scrum methodology and have experience in developing software within such a framework.

Requirements for "Social Skills"
All social skills required to fulfill the previously mentioned general task description as well as the following requirements:
The employee has intermediate-level experience, typically between 2 and 5 years, in their role or in the tasks performed by the group. Alternatively, the role and responsibilities require solid, but not exceptional, expertise.
The activities are associated with a moderate level of complexity and responsibility and require the ability to work independently.
The project may be carried out at either a national or international level.

Other Requirements & Relativizations
Skill Sets
Core Technical Skills

• Python (expert-level coding, modular programming, debugging, performance optimization)
• PySpark (distributed processing, tuning, handling large-scale data, performance optimization)
• Google Cloud Platform (GCP):
• BigQuery – advanced SQL, optimization, partitioning, clustering, cost management
• DataProc – Spark/Hadoop cluster management, job orchestration
• Dataflow – streaming & batch pipelines, real-time processing

Big Data & Cloud Skills

• Strong understanding of big data technologies (distributed computing, Hadoop/Spark ecosystem, data lakes, Kafka, batch & stream processing)
• Experience with data modeling and designing scalable data pipelines
• Familiarity with cloud-native architectures and serverless processing
•  

Analytical & Problem-Solving Skills:

• Ability to analyze complex data sets and derive insights
• Strong logical and critical thinking
• Experience optimizing query performance and pipeline efficiency
• Comfortable troubleshooting large-scale data workflows
• Preferred / Nice-to-Have Skills
• Experience with CI/CD pipelines for data engineering workflows
• Workflow orchestration: Airflow, Cloud Composer, Dagster
• Data governance, data quality frameworks, and compliance standards
• AI & Advanced Analytics Exposure
• Familiarity with Large Language Models (LLMs) and their applications in data workflows
• General understanding of AI/ML concepts and how they integrate with data engineering pipelines
• Exposure to MLOps tools or frameworks for deploying and monitoring AI models

Soft Skills

• Strong communication and collaboration with cross-functional teams
• Ability to work in Agile environments
• Mentoring and guiding junior engineers
• Self-driven, proactive learner, and adaptable to emerging technologies

We're looking for a Computer Vision Engineer to build visual intelligence features for a product that runs across connected devices and mobile apps.

This is a hands-on engineering role, not a research one: you'll take CV models from training to production, with a strong focus on making them run fast on resource-constrained devices.

What you'll do:

• Design, train, and deploy CV models - object detection, classification, segmentation, visual analysis;
• Optimize models for edge and mobile inference (quantization, pruning, distillation);
• Build data pipelines for image and video processing, including annotation and dataset versioning;
• Set up monitoring and retraining workflows for models in production;
• Work with backend and mobile teams to integrate model outputs into user-facing features.

Requirements:

• 3+ years in applied computer vision or ML with a strong CV focus;
• Solid deep learning background: CNNs, detection and segmentation architectures (YOLO, Faster R-CNN, U-Net), vision transformers;
• Python, PyTorch or TensorFlow, OpenCV;
• Experience deploying and maintaining CV models in production;
• Hands-on with on-device inference: ONNX, TensorRT, TensorFlow Lite, Core ML, or OpenVINO;
• Cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• English: Intermediate or higher.

Nice to have:

• MLOps tools (MLflow, Airflow, Vertex AI);
• Real-time video processing at scale (FFmpeg, GStreamer, Kafka);
• Edge AI or mobile ML experience.

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

Responsibilities:

- Design, build, and maintain ETL/ELT pipelines on AWS
- Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
- Develop and optimize data models for data warehouse/data lake (Redshift, S3)
- Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
- Implement data quality checks, validation, and reconciliation processes
- Optimize pipeline performance and manage compute/storage costs
- Ensure data security and access control (IAM, KMS, encryption)
- Monitor and troubleshoot pipeline failures (CloudWatch, logging)
- Collaborate with data analysts, BI developers, and architects on data requirements
- Document data pipelines, architecture, and operational runbooks

Mandatory Skills Description:

- 5+ years of experience
- Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
- Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
- Proficiency in Python or Scala for data engineering
- Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
- Experience migrating data pipelines from on-prem or other cloud platforms
- Understanding of data governance, security, and access control on AWS
- Experience with CI/CD for data pipelines

Nice-to-Have Skills Description:

- AWS certification (Data Analytics Specialty or Data Engineer Associate)
- Experience with streaming data (Kinesis, MSK/Kafka)
- Familiarity with Infrastructure as Code (Terraform/CloudFormation)
- Experience with orchestration tools (Airflow, dbt)
- Experience in financial domain

Languages:

English: C2 Proficient

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

Responsibilities:

-Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers
-HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads
-ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval
-Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)
Pattern definition — define reusable architecture and migration patterns for use across teams/pods
-Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

Mandatory Skills:

AWS Database Migration Service

Mandatory Skills Description:

- 5+ years experience
- hands-on experince with AWS Migration
- AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services
- IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform
- Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale
- PySpark — hands-on experience with PySpark for large-scale data processing
- CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows
- AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design
- GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

Nice-to-Have Skills Description:

- AWS certification (Data Analytics Specialty or Solutions Architect)
- Experience with BI tools (QuickSight, Tableau, Power BI)
- Infrastructure as Code experience (Terraform/CloudFormation)
- Industry experience relevant to your business
- Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)
- experince in financial domain

Languages:

English: C2 Proficient

Project description

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

Responsibilities

-Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers

-HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads

-ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval

-Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)

Pattern definition — define reusable architecture and migration patterns for use across teams/pods

-Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

Skills

Must have

5+ years experience

hands-on experince with AWS Migration

AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services

IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform

Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale

PySpark — hands-on experience with PySpark for large-scale data processing

CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows

AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design

GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

Nice to have

AWS certification (Data Analytics Specialty or Solutions Architect)

Experience with BI tools (QuickSight, Tableau, Power BI)

Infrastructure as Code experience (Terraform/CloudFormation)

Industry experience relevant to your business

Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)

experince in financial domain

Other

Languages

English: C2 Proficient

• This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
  During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
  The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
  This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

• Responsibilities:

  - Design, build, and maintain ETL/ELT pipelines on AWS
  - Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
  - Develop and optimize data models for data warehouse/data lake (Redshift, S3)
  - Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
  - Implement data quality checks, validation, and reconciliation processes
  - Optimize pipeline performance and manage compute/storage costs
  - Ensure data security and access control (IAM, KMS, encryption)
  - Monitor and troubleshoot pipeline failures (CloudWatch, logging)
  - Collaborate with data analysts, BI developers, and architects on data requirements
  - Document data pipelines, architecture, and operational runbooks

• Mandatory Skills Description:

  - 5+ years of experience
  - Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
  - Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
  - Proficiency in Python or Scala for data engineering
  - Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
  - Experience migrating data pipelines from on-prem or other cloud platforms
  - Understanding of data governance, security, and access control on AWS
  - Experience with CI/CD for data pipelines

• Nice-to-Have Skills Description:

  - AWS certification (Data Analytics Specialty or Data Engineer Associate)
  - Experience with streaming data (Kinesis, MSK/Kafka)
  - Familiarity with Infrastructure as Code (Terraform/CloudFormation)
  - Experience with orchestration tools (Airflow, dbt)
  - Experience in financial domain

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

• Responsibilities:

  -Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers
  -HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads
  -ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval
  -Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)
  Pattern definition — define reusable architecture and migration patterns for use across teams/pods
  -Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

• Mandatory Skills Description:

  - 5+ years experience
  - hands-on experince with AWS Migration
  - AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services
  - IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform
  - Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale
  - PySpark — hands-on experience with PySpark for large-scale data processing
  - CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows
  - AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design
  - GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

• Nice-to-Have Skills Description:

  - AWS certification (Data Analytics Specialty or Solutions Architect)
  - Experience with BI tools (QuickSight, Tableau, Power BI)
  - Infrastructure as Code experience (Terraform/CloudFormation)
  - Industry experience relevant to your business
  - Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)
  - experince in financial domain

Key details:
Start date: 1 October 2026
Duration: initially until 31 December 2026
Option to extend: until 31 December 2027
Workload: approx. 400 hours in 2026, with an option to extend to approx. 1,680 hours in 2027
Location: predominantly remote (approx. 80%), with occasional assignments in Hanover or at the client’s premises.

Project background: As part of an AI and machine learning initiative, we are driving forward the further development and support of data-driven solutions for various specialist areas within the social security sector. We are seeking support in the development, optimisation and operation of modern data and AI platforms.

Responsibilities:

• Carrying out data collection, data cleaning and data transformation
• Creating and maintaining dashboards for data visualisation
• Setting up and maintaining data pipelines
• Identifying and testing new areas of application for AI-based solutions

Requirements:

• At least 2 years’ experience with SQL, data transformations, data pipelines and data storage (DB/DWH)
• At least 2 years’ practical experience in software development using Python
• At least 2 years’ experience in developing ETL pipelines
• At least 2 years’ experience in data-driven projects (please specify the project objective of each reference project, your role and the tasks you carried out)
• At least 2 years’ experience in agile development teams
• German language skills at C1 level or higher !!!

Desirable:

• Two years’ professional experience in CI/CD development
• Two years’ professional experience with Jenkins and ArgoCD
• Two years’ professional experience with Argo Workflows
• Two years’ professional experience with Kubeflow or OpenShift AI in a machine learning environment
• Two years’ professional experience with machine learning and the practical application of ML models
• Two years’ professional experience with unit and integration testing
• Two years’ experience in the field of statutory social insurance
• Two years’ project work in a SAFe environment

Job Description
Extensive software development experience, including experience working on complex backend or data infrastructure projects.
Strong hands-on experience with Scala.

Experience with Apache Iceberg or similar table formats and data lake technologies.

Hands-on experience developing backend services, data-processing engines, infrastructure components.
Programming experience in C /or C++ is highly desirable / preferred, the role is primarily focused on backend/system-level engineering rather than full-stack application development.
Deep understanding of distributed systems, data processing, storage engines, or data infrastructure.

Strong knowledge of data consistency, transactions, schema evolution, partitioning, compaction, and performance optimization.

Proven ability to troubleshoot complex production and customer issues.

Strong ownership mindset and the ability to operate effectively in areas with limited documentation or historical knowledge.

Excellent communication and collaboration skills.

Preferred Qualifications: 
Big Data Frameworks: Hands-on experience with distributed data-processing technologies such as Apache Spark, Flink, Trino, or Presto.
Cloud Object Storage: Experience working with cloud storage platforms, including Amazon S3, Google Cloud Storage (GCS), or Azure Data Lake Storage (ADLS).
Legacy Modernization: Proven track record of modernizing or gradually improving a mature codebase without disrupting existing customers.
Job Responsibilities
Architect & Build: Lead the architecture, design, and implementation of highly scalable, complex backend systems and robust data infrastructure using Scala.
Data Lake Engineering: Design and manage enterprise-grade data lake solutions leveraging Apache Iceberg or equivalent table formats, ensuring optimal data consistency, transactions, schema evolution, partitioning, and compaction.
Performance Optimization: Maximize system throughput, minimize latency, and optimize resource utilization across large-scale distributed data processing systems.
Production Excellence: Systematically diagnose, troubleshoot, and resolve complex, high-impact production and customer-facing infrastructure issues.
Codebase Modernization: Champion the gradual improvement and modernization of a mature, production-critical codebase without causing disruption to active customers or services.
Technical Leadership & Collaboration: Act as a technical authority, fostering strong communication and collaboration across engineering teams to align on technical objectives.
Navigate Ambiguity: Exercise a strong ownership mindset to operate effectively and deliver high-quality outcomes in areas with limited documentation or historical knowledge.
Department/Project Description
We are seeking an expert Scala Engineer to design, optimize, and scale our next-generation multi-cloud data lake house architecture across AWS and Azure. The ideal candidate is a backend/system engineer with strong experience building engines, infrastructure, or high-performance backend components. In this role, you will leverage Qlik Open Lakehouse (integrated with Qlik Talend Cloud) to establish a low-overhead, high-throughput data foundation. You will manage automated data ingestion, implement continuous table optimization, handle cross-cloud data mirroring, and ensure seamless multi-engine access without vendor lock-in.

About

We’re looking for a senior ML/AI Engineer to build Stream, a new conversational interface. As an ML/AI engineer, you will architect, build, and deploy a new interface which spans voice, audio, and agentic control. This role is perfect for someone who cares deeply about real-world ML deployment and low-latency voice interactions.

Responsibilities

• Design and improve user-facing experiences, including voice, audio, and agentic control.
• Develop, evaluate, and deploy models spanning cloud models to resource-constrained on-device settings
• Optimize inference pipelines for latency, reliability, and concurrency

• Work closely with other ML/AI engineers, infrastructure engineers, designers, and cofounders on existing and future products

   

Qualifications

• 4+ years in artificial intelligence or machine learning applications
• Experience shipping ML-based products is required
• Experience in large-language models is required
• Experience in audio / voice ML is a major plus
• Comfortable writing production-grade software
• Passion for human-computer interaction

T H E  R O L E
Own the intelligence layer of Rx360 — the models that turn continuous sensor and medication data into a working understanding of each individual we serve, and turn that understanding into actions that help them stay independent. You will set the ML architecture, build the longitudinal modeling stack from raw signal through to user-facing recommendation, and define how our models and our conversational companion work together.

W H AT Y O U ' L L D O
Own the ML architecture for personalized health intelligence — per-user baselines, N-of-1 and hierarchical models, and the strategy for learning from one individual's history rather than a population mean.
Build the digital biomarker stack on raw wearable signal: gait and cadence, sit-to-stand and postural transitions, activity classification, sleep and circadian structure, and cardiovascular trend from IMU and PPG streams.
Design change detection and event-association models that link medication starts, dose adjustments, and adherence gaps to downstream behavioral shifts — with defensible handling of confounding, lag structure, and multiple comparisons.
Turn a detected change into a recommendation: what to surface, to whom (the individual, family, pharmacist, or provider), with what urgency — and when the right answer is to say nothing.
Build the data and training infrastructure that carries high-frequency time-series from device ingestion through feature generation, training, serving, and monitoring.
Own evaluation. Offline benchmarks, labeled ground truth from pilot cohorts, calibration and drift monitoring, and the false-positive budget that decides whether people trust what we tell them.
Ground our conversational companion in real signal. Our AI companion runs on a large language model inside our
HIPAA-compliant cloud environment; you will connect it to per-user model output and own the evaluation and post- training work that keeps its guidance accurate, useful, and safe.
Push inference to the edge where latency, privacy, or battery demand it, working directly with the firmware and hardware teams on what runs on the band versus in the cloud.
Direct AI coding agents to accelerate implementation while holding a rigorous quality bar — the way the rest of our engineering team works.
Partner with clinical collaborators, including our university pilot, to design studies that produce the labeled data these models actually need.
Set technical direction and raise the bar for ML across the team.

R E Q U I R E D
6+ years in machine learning, including systems you have shipped to real users and operated in production — not only research or prototypes.
Deep experience with time-series or sensor data: physiological signals, IMU / accelerometry, or comparable high- frequency multivariate streams.
Demonstrated work in individual-level modeling — per-subject baselines, hierarchical or Bayesian methods, anomaly and change-point detection, or models that adapt as data from a single person accumulates.
Strong applied statistical judgment. You can separate a real effect from a coincidence in observational data, and you know exactly where a causal claim stops being defensible.
Production-grade Python and PyTorch (or equivalent). You write software other engineers are willing to maintain.
Ownership of the full lifecycle: data pipelines, training infrastructure, deployment, monitoring, and retraining.
Experience with large-language-model evaluation and post-training — grounding and retrieval, structured output, eval harnesses, and guardrails.
Cloud-native ML at production scale (AWS preferred).
Fluent use of AI development tools (Claude Code, Cursor, Copilot, or equivalent) with the domain depth to catch where generated code fails silently.
Clear, direct communication. You can set direction across firmware, backend, mobile, design, and clinical stakeholders.

N I C E T O H AV E
Wearables, digital biomarkers, gait analysis, fall risk, or human activity recognition.
Self-supervised or foundation-model approaches for wearable time series, and comfort working under real label scarcity.
Causal inference on observational data — interrupted time series, difference-in-differences, or synthetic control.
Medication-effect modeling: polypharmacy, adherence, or PK/PD intuition.
On-device and edge ML — quantization, distillation, TinyML, inference under a power budget.
Regulated environments: HIPAA and PHI handling, model governance, or the general-wellness boundary.
Clinical research collaboration and IRB-governed data.
Voice or audio ML.

W H Y T H I S R O L E I S D I F F E R E N T
G R E E N F I E L D
You are defining the intelligence layer, not maintaining someone
else's. The architecture decisions are yours to make.
R E A L S I G N A L
Continuous, longitudinal, multi- stream data from a device we build
ourselves — with the firmware team a message away.
I T M AT T E R S
A model that catches a medication effect three days early changes
whether someone keeps living in their own home.

H O W W E W O R K
Mission comes first.
The work has real impact on people's lives. We take that
responsibility seriously.
Thoughtful technology.
Technology should simplify life, not complicate it.
Respect for independence.
The people we build for value autonomy and dignity.
Everything we create reflects that.
Curiosity.
We encourage questions, experimentation, and new ways
of thinking.

A B O U T R X 3 6 0
Rx360 is a wellness platform for older adults who want to keep living on their own terms — and for the families, pharmacists, and providers around them.
The wearable is a wrist-worn band we design in-house, carrying a PPG sensor for cardiovascular signal, a six-axis IMU for motion, gait, and posture, and on-device fall detection. The iOS and Android apps give the individual a plain- language view of their own day and their medications, and open a shared circle of care to the family members they choose to include.
Rx360 is a U.S. company headquartered in Los Angeles, California, with a distributed engineering team. Hardware, firmware, mobile, backend, and machine learning are all in-house.

Stay Group is an innovative Marketing company with a mission to help businesses acquire their target customers and create top-tier solutions in the iGaming and entertainment industry. Currently, we are looking for our perfect DevOps Engineer to join our team and help us build scalable, reliable, and secure infrastructure.

Requirements:

• 3+ years of commercial experience as a DevOps Engineer.
• Strong hands-on experience with Kubernetes (GKE, EKS, and self-hosted clusters).
• Experience deploying and maintaining cloud infrastructure on AWS and/or Google Cloud Platform.
• Experience with CI/CD pipelines using GitLab CI/CD.
• Strong knowledge of Docker and containerized application deployment.
• Experience configuring and managing Cloudflare services, including WAF, SSL/TLS, DNS, and API automation.
• Experience with monitoring, logging, and observability tools:
  • Prometheus,
  • Grafana,
  • ELK Stack.
• Experience with database administration:
  • MySQL,
  • PostgreSQL,
  • MongoDB.
• Understanding of cloud cost optimization (FinOps).
• Experience with Infrastructure as Code and automation.
• Strong troubleshooting, analytical thinking, and ownership mindset.
• English level sufficient for reading technical documentation and communicating with international teams.

Will be a plus:

• Experience with Datadog (APM, RUM, Logs).
• Experience configuring Global Load Balancers in Google Cloud.
• Experience building scalable, high-load infrastructure.

Key Responsibilities:

• Design, build, and maintain highly available cloud infrastructure.
• Configure and manage Cloudflare services, including WAF, SSL/TLS, DNS, and API automation.
• Deploy, maintain, and optimize Kubernetes clusters across GKE, EKS, and self-hosted environments.
• Build and improve CI/CD pipelines using GitLab CI/CD.
• Automate Docker image builds and deployment processes.
• Implement and maintain monitoring, logging, alerting, and observability solutions using Prometheus, Grafana, and ELK.
• Optimize cloud infrastructure performance, scalability, and cost efficiency.
• Configure auto-scaling and load balancing for cloud environments.
• Administer and support production databases.
• Collaborate closely with Development, QA, Product, and Security teams to improve infrastructure reliability and delivery processes.

Benefits

Freedom to choose where to work: work from anywhere in the world with the freedom of remote options.

Flexibility: enjoy flexible working hours that let you balance your personal and professional life while staying focused on delivering great results.

Corporate English speaking club: improve your English proficiency with regular practice and expert guidance.

A Culture That Cares: thrive in a friendly environment where a strong corporate culture builds connections, drives innovation, and makes you feel truly valued.

Accelerate Your Growth: seize career advancement opportunities in a rapidly expanding marketing company. As the company scales, so will your professional journey.