Jobs Data Engineer

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.

About Farsight Vision:

Farsight Vision develops AI-powered geospatial intelligence solutions for defence and security operations. The platform transforms raw aerial and sensor data into real-time 3D environments, actionable analytics, and AI-assisted operational scenarios, enabling enhanced situational awareness, mission planning, and faster decision-making for both human operators and autonomous systems. We operate at the intersection of geospatial data, computer vision, and defense operations – and if that interests you, read on.

We are looking for a Robotics Engineer to own motion control for our unmanned ground vehicle (UGV) end-to-end – from control theory on paper to a robot that actually moves the way you designed it to. This is not a role for someone who wants control loops handed to them pre-tuned: you will design, implement, and validate them on hardware yourself

Responsibilities:   

- Implement navigation and control algorithms for different types of UGVs: differential drive, tracked wheels, Ackermann steering, etc

- Design and implement control algorithms (PID and beyond) that translate navigation commands into reliable robot motion

- Own the control stack end-to-end – from algorithm design through tuning and validation on the physical unit

- Simulate physical driving for different types of wheels and terrains before deploying to hardware

- Work closely with the Robotics team on actuator and sensor integration, since control quality is only as good as what it's built on

- Debug control issues that only show up in the field, not in simulation – this role is hands-on with the physical robot in Lviv

- Collaborate with the Perception Engineer so control decisions correctly account for obstacle and environment data

- Propose architecture and design decisions rather than waiting to be handed a fully-scoped ticket: this is a small team building a new capability almost no one in the Ukrainian market has shipped yet

 
Requirements:   

- Strong mathematical foundation: linear algebra, calculus, and the algorithmic thinking control systems are built on

- Practical experience with control theory – PID control at minimum, ideally more advanced control approaches

- Background in robotics, guidance, or navigation systems (e.g., autonomous vehicle guidance, drone terminal guidance, or similar)

- Experience with physics simulators (e.g. Gazebo, NVIDIA Isaac, MATLAB, Simulink), with the ability to simulate dynamic models and verify controller performance before deployment

- Experience working with sensors such as IMU, LiDAR, etc.

- Knowledge of state estimation and filtering (e.g. Kalman filter, extended Kalman filter, complementary filter)

- Solid programming skills in C++ (and Python for prototyping)

- Good OOP skills — ability to write and design clean code that's flexible to extend and maintain

- Comfortable working close to hardware, not purely in a software abstraction layer

- Upper-Intermediate+ English

Nice to have:

- Experience with ROS/ROS2

- Path planning algorithms (A*, D*, RRT, etc.)

- Prior work on UAV or UGV guidance/navigation systems

- Defense-tech or military-tech industry exposure

- Embedded systems or hardware-adjacent experience

 
Security Eligibility

Our platform is actively used by the Armed Forces in live operations. As an Estonian-Ukrainian defence technology company handling sensitive operational data, we have a responsibility – legal, ethical, and practical – to ensure the integrity of everyone on our team.

All candidates must be able to confirm the following:

Neither you nor your close relatives (parents, spouse, siblings, children) reside in Russia, Belarus, or temporarily occupied Ukrainian territories

You have no regular personal, financial, business, or professional contact with individuals in those territories

You consent to a deeper security verification if the process advances to later stages

If any of the above criteria are not met, please do not proceed with your application.

 
Why Farsight Vision   

We are a startup operating under real-world conditions – our product is in active deployment, and the pace reflects that. We are not a company where you get to run the same experiment three times until it's elegant – we need someone who wants to see their control loop working on a real robot within weeks, not quarters, and who is motivated by the fact that what you build here gets used, not shelved.

Flexible working hours. Paid vacation and sick leave. Direct access to founders and leadership.

Are you passionate about building scalable Big Data solutions and optimizing high-load platforms? We are looking for a Senior Big Data Engineer to join a long-term project in the cybersecurity and AdTech domain focused on real-time risk assessment for digital advertising ecosystems.

We at Sigma Software offer the opportunity to work on impactful products, collaborate with experienced engineering teams, and contribute to solutions operating on large-scale datasets in a fast-paced environment. You will have the freedom to influence technical decisions and drive meaningful improvements across the platform.

CUSTOMER

Our Customer operates in the cybersecurity domain and develops solutions aimed at protecting organizations and digital ecosystems from online risks and vulnerabilities. The company focuses on building advanced technologies that improve security, reliability, and operational resilience across various industries. 

PROJECT

The project is a real-time risk assessment platform for digital advertising ecosystems. The solution helps customers identify potentially low-quality or fraudulent traffic and automatically make decisions before allocating advertising budgets. The platform processes large-scale data streams and requires continuous improvements in performance, scalability, and cost efficiency.

At the initial stage, the role will focus on Snowflake optimization and identifying inefficiencies in data pipelines and SQL queries. In the long term, the engineer will contribute to backend development, platform architecture improvements, and implementation of scalable Java-based services.

RESPONSIBILITIES:

• Optimize Snowflake usage and improve infrastructure cost efficiency
• Identify inefficiencies in data pipelines and SQL queries
• Implement performance and cost optimization improvements
• Enhance and maintain Java-based services
• Implement and support new platform features
• Improve existing engineering processes and platform components
• Develop scalable and reliable solutions for high-load environments
• Contribute to architecture improvements and implementation of new components
• Collaborate with distributed engineering teams to deliver stable platform functionality

REQUIREMENTS: 

• At least 5 years of commercial experience in Big Data engineering
• Strong hands-on experience with Snowflake
• Strong expertise in Python
• Strong expertise in Java
• Advanced SQL knowledge and experience optimizing complex queries
• Solid Computer Science fundamentals
• Upper-Intermediate or higher English level

WILL BE A PLUS

• Experience with AWS cloud services
• Experience working with Amazon S3
• Experience in AdTech, cybersecurity, fraud detection, or risk assessment domains

PERSONAL PROFILE

• Proactive and ownership-driven mindset
• Strong analytical and troubleshooting skills
• Ability to work effectively in fast-paced environments
• Team-oriented communication style
• Focus on scalable and maintainable engineering solutions

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.

We are looking for a Senior Data Engineer with specialized domain expertise in fraud detection, Anti-Money Laundering (AML), or transaction-monitoring systems to establish data-quality foundations for decision engines.

Our client is a fast-growing European FinTech company in the Business Spend Management space - providing corporate cards and related financial products to SME and mid-sized businesses across the EU and UK in a regulated environment.
You will embed directly into a product squad as a hands-on Individual Contributor (IC). 

Key Responsibilities:

• Architect and build robust data-quality frameworks specifically designed to power decision engines.
• Design, scale, and maintain reliable, high-throughput production data pipelines.
• Build infrastructure and workflows that enable fast, seamless deployment of fraud and transaction-monitoring rules.
• Bring deep domain knowledge in Fraud, AML, and Transaction Monitoring to production data engineering workflows.
• Multiply the output and quality of your squad while sharing data engineering patterns and best practices across the organization.

Requirements:

• Strong, senior-level 5+ years expertise in Python for data engineering and production pipeline development.
• Experience/background in Fraud, AML, or Transaction-Monitoring systems.
• Proven track record of establishing data-quality foundations for automated decisioning systems.
• Extensive experience building production data pipelines and supporting rapid rule deployment workflows.

Nice-to-Have Skills:

• Hands-on experience with Airflow, SQL, PostgreSQL, and Google BigQuery (standard stack for data/analytics workloads).
• Experience working in hybrid cloud environments (AWS primary + GCP analytics).
• Familiarity with modern engineering workflows (Linear, GitHub, Notion, Slack) and AI-assisted development (Claude Code, GitHub Copilot).

The project is for one of the world's famous science and technology companies in pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future. Modernizing the data infrastructure through the transition to Snowflake as a priority, as it will enhance capabilities for implementing advanced AI solutions and unlock numerous opportunities for innovation and growth.

We are seeking a highly skilled Snowflake Data Engineer to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

• Responsibilities:

  • In-depth knowledge of Snowflake's data warehousing capabilities.
  • Understanding of Snowflake's virtual warehouse architecture and how to optimize performance
  and cost.
  • Proficiency in using Snowflake's data sharing and integration features for seamless collaboration.
  • Develop and optimize complex SQL scripts, stored procedures, and data transformations.
  • Work closely with data analysts, architects, and business teams to understand requirements and
  deliver reliable data solutions.
  • Implement and maintain data models, dimensional modeling for data warehousing, data marts,
  and star/snowflake schemas to support reporting and analytics.
  • Integrate data from various sources including APIs, flat files, relational databases, and cloud
  services.
  • Ensure data quality, data governance, and compliance standards are met.
  • Monitor and troubleshoot performance issues, errors, and pipeline failures in Snowflake and
  associated tools.
  • Participate in code reviews, testing, and deployment of data solutions in development and production environments.

• Mandatory Skills Description:

  • 5+ years of experience
  • Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
  • Ability to write complex SQL queries, stored procedures, and user-defined functions.
  • Skills in optimizing SQL queries for performance and efficiency.
  • Experience with ETL/ELT tools and techniques, including Snowpipe, AWS Glue, openflow, fivetran
  or similar tools for real-time and periodic data processing.
  • Proficiency in transforming data within Snowflake using SQL, with Python being a plus.
  • Strong understanding of data security, compliance and governance.
  • Experience with DBT for database object modeling and provisioning.
  • Experience in version control tools, particularly Azure DevOps.
  • Good documentation and coaching practice.

Important: after confirming your application on this platform, you’ll receive an email with the next step: completing your application on our internal site, LaunchPod. So keep an eye on your inbox and don’t miss this step — without it, the process can’t move forward.

Why join us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! :)

About the role
We are looking for an experienced Senior Databricks Data Engineer to help modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will build batch and streaming pipelines with PySpark and Delta Lake, following a medallion architecture across bronze, silver, and gold layers. This role also uses AI tools like Claude and GitHub Copilot to speed up development.

What you will do

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or Github Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Participate in Agile or product-centric delivery practices including sprint planning and retrospectives.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

Must haves

• 4+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills, including mentoring junior engineers and explaining data concepts to non-technical stakeholders.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
  Familiarity with secure coding standards and industry security best practices.
• Experience delivering production data platforms at scale.
• Upper-intermediate English level.

Nice to haves

• Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure Devops.
• Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.
• Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).
• Experience working in Agile or team-based development environments preferred.

Perks and benefits

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office — whatever makes you the happiest and most productive.

Meet Our Recruitment Process

Asynchronous stage — An automated, self-paced track that helps us move faster and give you quicker feedback:

• Short online form to confirm basic requirements
• 30–60 minute skills assessment
• 5-minute introduction video

Synchronous stage — Live interviews

• Technical interview with our engineering team (scheduled at your convenience)
• Final interview with your future teammates
   

If it’s a match—you’ll get an offer!

Important: after confirming your application on this platform, you’ll receive an email with the next step: completing your application on our internal site, LaunchPod. So keep an eye on your inbox and don’t miss this step — without it, the process can’t move forward.

About the role

We are looking for a Middle Data Engineer to help modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will build batch and streaming pipelines with PySpark and Delta Lake, following a medallion architecture across bronze, silver, and gold layers. This role also uses AI tools like Claude and GitHub Copilot to speed up development.

What you will do

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or Github Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

Must haves

• 3+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.
• Experience delivering production data platforms at scale.
• Upper-intermediate English level.

Nice to haves

• Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure DevOps.
• Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.
• Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).
• Experience working in Agile or team-based development environments preferred.

The benefits of joining us

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Meet Our Recruitment Process

Asynchronous stage – An automated, self-paced track that helps us move faster and give you quicker feedback:

• Short online form to confirm basic requirements
• 30–60 minute skills assessment via Codility – a platform founded in Poland that helps us provide quicker feedback and streamline this stage of the process.
• 5-minute introduction video

Synchronous stage – Live interviews

• Technical interview with our engineering team (scheduled at your convenience)
• Final interview with your future teammates

If it’s a match — you’ll get an offer!

We’re building a next-generation payment platform from the ground up (0→1) — and we’re looking for a Senior Payment Infrastructure Engineer with deep expertise in fiat/card payments to take full ownership of the core system.

This is not about plugging APIs. This is about building real payment infrastructure.

🚀 What You’ll Do

• Build and own core fiat/card payment infrastructure
• Integrate with acquirers, PSPs, and payment processors
• Design and debug end-to-end payment flows (auth, capture, settlement, refunds)
• Work with 3DS2, MIT, recurring payments, and card-on-file systems
• Architect scalable billing and payment systems from scratch
• Take full ownership as the first (solo) engineer in this domain

🧠 What We’re Looking For

• 5+ years of hands-on production experience in card payments
• Strong experience with PHP and/or Go
• Deep understanding of payment lifecycle and card network specifications
• Practical experience with:
  • 3DS2
  • Merchant-initiated transactions (MIT)
  • Recurring payments
  • Card-on-file flows
• Strong debugging skills (request/response level, transaction tracing)
• Ability to build systems independently from 0→1
• AI-native engineering mindset 🤖

⭐ Bonus Points

• Experience as MoR (Merchant of Record) or PayFac
• Multi-acquirer / international payment setups
• Experience building payment infrastructure (not just integrations)
• Startup or early-stage product experience

💼 What We Offer

• Full ownership of a core payment platform
• High-impact engineering work with zero bureaucracy
• Opportunity to build critical infrastructure from scratch
• AI-first engineering environment

We are looking for Forward Deployed Engineer. 
Our client is a venture-backed Industrial AI startup building the technology behind the self-aware factory.

As a Forward Deployed Engineer, you will work directly with industrial customers, taking projects from initial discovery through deployment and expansion.

This is a highly hands-on, customer-facing engineering role. You will understand how a customer's factory operates, identify valuable use cases, connect their systems and data, and build and deploy AI-powered workflows that deliver measurable business impact.
 

Responsibilities:

• Work directly with customers to understand their factories, processes, systems and operational challenges
• Lead technical discovery sessions, workshops and use-case definition with engineering, operations and IT teams
• Translate manufacturing problems into practical AI agents, workflows and technical solutions
• Build, configure and deploy agents for production, quality, maintenance, logistics and engineering use cases
• Integrate industrial and enterprise data from PLCs, OPC UA, MQTT, SCADA, historians, MES, ERP, QMS, CMMS, databases and APIs
• Connect and contextualize customer data within the Context Graph
• Work closely with customers during pilots, deployments and rollouts
• Troubleshoot technical issues and continuously improve deployed solutions
• Identify recurring customer needs and translate them into requirements for the product and engineering teams
• Support technical sales, solution discovery and early customer engagements
   

Requirements:

• Strong experience with Python, APIs, databases and/or data integration
• Experience working with complex technical systems and integrating different data sources
• A product mindset and genuine curiosity about understanding customer problems
• The ability to communicate effectively with engineers, plant teams, operations leaders and enterprise IT
• Experience in manufacturing, industrial technology, AI/ML, AI agents or enterprise software is a strong advantage
• An entrepreneurial mindset and the ability to work independently in an ambiguous, fast-moving environment
• Strong ownership and a willingness to take responsibility for the entire customer outcome
• The ability and willingness to travel regularly to customer sites

Background

• Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, Data Science or a related field — or equivalent practical experience
• Strong communication and problem-solving skills
• Comfortable working independently and taking initiative
• Fluent English
• Location Munich
   

Interested?

If you have a strong technical background and enjoy solving complex problems directly with customers, we'd be happy to hear from you.

On behalf of our client, we are looking for a Senior Infrastructure Engineer (GPU Platform)

Requirements:

- Production experience with multi-node GPU training infrastructure

- Strong Linux, containers, CUDA, and NVIDIA GPU stack knowledge

- Hands-on experience with NCCL and InfiniBand or RoCE/RDMA troubleshooting

- Deep experience with Kubernetes or Slurm

- Experience with infrastructure automation and observability

- Experience diagnosing issues across training workloads, networking, storage, and GPU hosts

- Strong incident leadership and provider-facing communication skills

- English – Upper-Intermediate or higher

Would be a plus:

- Experience in an AI lab, HPC environment, or specialist GPU cloud

- PyTorch, Megatron, DeepSpeed, or other distributed-training frameworks

- Experience with parallel storage and checkpoint optimization

- Experience working with multi-provider GPU platforms

Company offers:

- Long-term employment with possibilities for professional growth

- Fully remote work

- Reasonably flexible schedule

- 15 days of paid vacation

- Regular performance reviews