Jobs Data Engineer

Expected responsibilities:

• Designing and building scalable data pipelines (batch + streaming)
• Developing data infrastructure to support analytics and ML workflows
• Building secure, scalable APIs for data and model access
• Implementing event-driven architectures (Kafka) and orchestration (Airflow)
• Supporting Data Science and ML teams with platform capabilities
• Ensuring data observability, governance, and system reliability

We are looking for strong, recent hands-on experience (last 2–3 years) with:
Core stack:

• Kafka (streaming)
• Airflow (orchestration)
• SQL + NoSQL databases
• Cloud platforms (AWS preferred)
• API development

Infrastructure:

• Docker / Kubernetes
• Infrastructure as Code (Terraform / CloudFormation)
• CI/CD pipelines
• Monitoring & observability

Bonus:

• Experience supporting ML pipelines
• Exposure to tools like SageMaker or OpenAI

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

As a global leader in IT services, PwC is supported by PwC Poland’s Data, Digital Solutions & AI team. We provide first-class services in all areas of Data Platforms, Data Analytics, Data Strategy and Data Management, just to name a few of our core capabilities. We support clients in the transition to and their adoption of modern data infrastructure, creation of new business models and streamlining their operational activities. 

Your future role:

• Building data platforms with critical components including data lakehouses, data warehouses and data lakes using tools like MS Fabric, Azure Databricks and Azure Data Factory for extraction, loading and transformation;
• Defining the flow of data through data platform from raw ingestion to business-ready consumption using most suitable data architecture patterns (e.g. medallion, lambda);
  Designing the migration plans for moving transactional and master data from legacy systems of record to systems of reference (new solutions);
• Conducting comprehensive data assessments alongside Data Modelers and Analysts across multiple systems of record and data domains to identify and remediate data quality issues;
• Liaising with client senior stakeholders and supporting Senior Data Architects to gather functional requirements to design modern data architecture;
• Supporting  Data Governance subject matter experts to define data standards and policies and specifically around Master Data Management and Data Quality;
• Some familiarizations with Data Science and Data Analytics use cases within the context of Consumer Goods, Retail, Manufacturing and/or Financial Service sectors will stand out.

Apply if you have:

• Minimum of 3 years in data engineering roles, although this can be considered alongside other relevant experience e.g., Business Intelligence, Data Science etc.;
• Hands‑on experience with data transformation platforms, with a strong preference for Azure‑based solutions such as Microsoft Fabric and Databricks; experience with other platforms (e.g. Snowflake, Azure Synapse, Google BigQuery) is also valued;
• Experience in building data warehouses, data lakes and data lakehouses using tools like Azure Data Factory and Azure Synapse Analytics;
• Familiarity with writing data transformation scripts using Python i.e. PySpark and SQL to query databases;
• Some experience in Data Engineering CI/CD practices and release management across Dev, QA and Production environments using tools like Azure DevOps, and/or Jira;
• Proficiency in Business English at B2 level or above, with confidence to write technical documentation e.g., designing documents with support from Project Manager;

• Experience with new technologies and AI‑based tools demonstrated in your daily work (e.g., task automation, information analysis, content creation).

   

Nice to have:

• Cloud certifications in Azure, AWS, or Google Cloud;
• Data Governance certifications e.g., DAMA;
• Evidence of training using online platforms e.g. Coursera, Udemy, Datacamp etc.;
• Application of data modelling frameworks including Kimball dimensional modelling to design and build logical and physical data models;
• Interest and some experience of experimenting with the application of Agentic AI to automate the extraction, transformation and loading of data using services like Azure Foundry;
• Proven experience in conducting data assessments across multiple data domains, including Product, and Customer and systems of record e.g. ERPs and CRMs;
• Experience of working on Data Strategy and Governance projects with a focus on improving Data Quality across key business functions;
• Familiarity with agile project management methodologies like Scrum to enable and scale minimal viable products;
• Some exposure to Data Science and Data Analytics use cases, particularly in the applications of Machine Learning and Generative AI techniques to help improve business forecasting, and demand planning;
• Experience in supporting senior stakeholders including Chief Data Officers in medium to large sized businesses across sectors such as Manufacturing, Consumer Goods, and/or Financial Services.
  
  Policy statements:
  https://www.pwc.com/ua/uk/about/privacy.html

Dataforest is looking for a Data Engineer to join an interesting software development project in the field of Cybersecurity. Our client’s platform offers Identity Insights drives real-time, multi-contextual digital forensics to protect new account opening workflows and expose fake accounts already in a customer database.

Requirements:
• 3+ years of hands-on experience in data engineering;
• 3+ years of commercial experience with Python;
• Advanced experience with SQL DBs (optimisations, monitoring, etc.);
• Advanced experience with PySpark;
• Solid understanding of ETL principles (architecture/ monitoring/ alerting/search and resolve bottlenecks);
• Familiar with AWS infrastructure (boto3, S3 buckets, etc);
• Experience working with large volumes of data;
• Understanding the principles of medallion architecture.

Responsibilities:
• Maintain data processing architecture using Python and PySpark that interacts with ScyllaDB & PostgreSQL.
• Work with Databricks infrastructure to ensure reliability, scalability, and cost efficiency.
• Proactively identify bottlenecks and suggest technical improvements.

Nice to have:

• Cassandra/Scylla

• PostgreSQL

• Pandas

• Experience with Kafka and Redis

We offer:

• Great networking opportunities with international clients, challenging tasks;

• Building interesting projects from scratch using new technologies;

• Personal and professional development opportunities;

• Competitive salary fixed in USD;

• Paid vacation and sick leaves;

• Flexible work schedule;

• Friendly working environment with minimal hierarchy;

• Team building activities, corporate events.

About us

Entravel is a member’s club for discounted luxury travel and whitelabel hotel platform provider focusing on the crypto industry, backed by VCs and prominent European founders. Our hotel product is incredibly strong; we offer more than one million properties, and our rates are amongst the lowest in the world. In fact, on most 5* hotels you won’t find a platform with lower rates than Entravel.

Your role

We are looking for a Data Engineer to automate and scale high-volume data workflows, with a strong focus on hotel and room mapping across multiple data sources.
This is an ownership-heavy role: you will take full responsibility for turning a manual, repetitive process into a reliable, scalable system and ensuring it runs correctly in production.

What you’ll do

• Own the end-to-end automation of hotel and room mapping workflows;
• Take responsibility for delivery, correctness, and reliability of the mapping outputs;
• Design and build data pipelines (ingestion → transformation → output);
• Translate manual mapping logic into scalable systems;
• Implement rule-based and fuzzy matching techniques for entity resolution;
• Leverage LLMs to improve mapping accuracy, classification, and edge-case handling;
• Normalize and clean inconsistent, messy real-world data;
• Build monitoring, validation, and retry mechanisms;
• Continuously improve pipeline performance and mapping accuracy.

Ownership & responsibility

• You own the mapping system in production;
• You are responsible for data quality, accuracy, and failure handling;
• You proactively identify gaps, edge cases, and inefficiencies;
• You define and evolve the mapping logic and data model;
• You ensure issues are detected, debugged, and resolved quickly;
• You drive the reduction of manual work and operational overhead.

What we’re looking for

• 3–6 years of experience in data engineering or similar roles;
• Strong Python and SQL;
• Experience building pipelines from scratch;
• Experience working with messy, third-party, or unstructured data;
• Experience with entity matching / deduplication / normalization;
• Hands-on experience using LLMs in production workflows;

Strong sense of ownership: you take responsibility for outcomes.

Nice to have

• Experience with Rust for performance-critical components;
• Familiarity with workflow orchestration tools (e.g. Airflow or similar);
• Experience with embeddings, vector search, or semantic matching;
• Background in travel, hospitality, or similar domains.

What success looks like

• Clear ownership of the mapping domain from day one;
• Significant reduction in manual work within the first 1–2 months;
• Stable, reliable pipelines running in production;
• Measurable improvement in mapping accuracy over time;
• Fast resolution of issues and continuous system improvements.

We offer

• Work schedule: full-time;
• Duration of the cooperation: Long-term;
• Flexible Start: Set your own comfortable working hours that accommodate your individual needs.
• Competitive Salary: High standards of remuneration reflecting your contribution and skills.
• Vacation: Enjoy 22 days of vacation for full rejuvenation and life enjoyment.
• Sick Leave: Ensure your health with 15 days of sick leave per year.
• Special Leave Cases: Support for significant life events such as weddings, childbirth, or the loss of a close family member.

We’re building agentic AI for real-world CX. As an AI Engineer at HiO, you’ll craft scalable, low-latency AI systems - from LLM orchestration to real-time APIs - using Python, LangGraph, and modern cloud stacks. Fast-paced, high-impact, and deeply hands-on.

What you’ll do

• Design, build, and maintain real-time AI-powered APIs and services in Python.
• Architect, optimize, and deploy LLM workflows leveraging LangGraph or similar orchestration frameworks.
• Develop agentic logic and reasoning pipelines for conversational and task-based automation.
• Build scalable infrastructure to support multi-model, low-latency, and streaming AI experiences.
• Collaborate with backend and product teams to translate complex customer workflows into reliable AI systems.
• Ensure high performance, reliability, and data security across production workloads.
• Deploy and manage applications on modern cloud environments (AWS / GCP)
• Implement monitoring, logging, and fault-tolerance mechanisms for production-grade AI systems.
• Contribute to architectural and product decisions, balancing innovation, safety, and scalability.

What We’re Looking For

• BSc or higher in Computer Science, Engineering, or a related technical field.
• 5+ years of experience in backend or ML engineering, building AI/LLM-based systems in production.
• Deep experience with Python (async, multiprocessing, decorators, type hints, debugging).
• Proven hands-on work with LLMs, including streaming inference, prompt management, or multi-agent systems.
• Familiarity with LangGraph, LangChain, or similar orchestration frameworks.
• Strong understanding of MLOps, microservices, and distributed architectures.
• Solid grasp of SQL/NoSQL databases, message queues, and scalable data design.
• Experience deploying applications to AWS, GCP, or similar cloud platforms.
• Ownership mindset - comfortable operating across the stack and figuring things out fast.
• Excited to work in a fast-paced startup environment where priorities shift quickly and impact is immediate.

We are looking for a Senior Data Engineer to join the Identity team at Equals5. This is not a standard ETL role. We are building a dynamic data ecosystem where AI is deeply integrated—both as a productivity multiplier and as a core component of our data processing logic for identity data enrichment and data scoring.

You will own the infrastructure that handles over 10,000 executions per minute, ensuring stability, scalability, and data integrity. You will work with a modern stack on Google Cloud Platform, utilizing Cloud Functions and Kubernetes.

We are looking for an engineer who improves infrastructure, automates everything, and is eager to implement LLM-based logic directly into high-load data flows.
 

Responsibilities

• AI-Driven Data Scoring: Design and implement pipelines that utilize LLMs to analyze and score identity data in real-time. You will integrate AI models directly into the decision-making loop, balancing accuracy with latency and cost.
• Own the Data Architecture: Architect scalable data solutions using GCP and Python. You will manage data storage and retrieval using BigQuery and Apache Iceberg to support querying of TBs of data.
• Heavy Data Processing: Utilize Apache Spark for data transformations and batch processing when lightweight cloud functions are not enough.
• Manage High-Load Orchestration: Maintain and optimize our system instances. It involves complex dataflows, custom Python nodes, and performance tuning for 10k+ execs/minute.
• Release Lifecycle (CI/CD): Take ownership of the deployment process, ensuring that updates to pipelines and infrastructure are released safely with proper testing and rollback strategies.
• Database Optimization: Manage PostgreSQL performance under heavy load, optimizing complex queries and indexing strategies.
• Active AI Usage: Use Claude Code and other AI engineering tools to accelerate your own development, refactoring, and testing processes.
• Incident Resolution: Proactively monitor the system. When alerts fire, you investigate the root cause—whether it’s a database lock or an LLM hallucination—and fix it permanently.
   

Requirements

• 4-5+ years of experience in Data Engineering or Backend Engineering with a strong data focus.
• Production AI Integration: Experience integrating LLMs (OpenAI, Anthropic, Gemini) into production applications via API. You understand latency, token limits, and how to structure data for AI scoring.
• Expertise in GCP: Understanding of Google Cloud Platform (Cloud Functions, IAM, Networking).
• Strong Python: You write clean, efficient, and testable code. You are comfortable building custom logic where standard tools fall short.
• Big Data Stack: Experience with BigQuery, Apache Spark, and modern table formats like Apache Iceberg.
• Kubernetes (K8s): Experience deploying and scaling services in containerized environments.
• Workflow Automation: Understanding of workflow orchestration tools at a deep technical level. N8N is a big part of our domain, so familiarity with it is highly valuable.
• PostgreSQL Mastery: Proven ability to handle heavy write/read loads and optimize schemas.
• English: B2+ (Upper-Intermediate) or higher.
   

Culture & Mindset

• Self-improvement: You are a fast learner. You don't fear AI replacing you; you master it to replace your manual tasks.
• Ownership: You treat the Identity domain as your own business. If a scoring model drifts or a pipeline slows down, you notice it and fix it without being asked.
• Internal Locus of Control: You take responsibility for outcomes. If an external API fails, you build a fallback mechanism instead of just blaming the provider.
• Get It Done: You prioritize shipping value. You know when to use a simple script and when to build a complex architecture.
• Openness: You share knowledge freely. If you find a better way to prompt the AI for scoring, you share it with the team.
   

What We Offer

• Fully remote with flexible hours (aligned with EU timezones for syncs).
• AI-Native Environment: We provide licenses for Claude Code and encourage using the bleeding edge of AI tech for both daily coding and product features.
• High-Impact Role: You will directly influence how we identify and score users, impacting the core business logic.
• Cross-functional visibility: Work closely with Product and Tech Leads to shape the future of Identity.
• No Bureaucracy: Fast decisions, no legacy processes, focus on results.

We are looking for a Data / Backend Engineer to architect and scale the core data infrastructure behind our healthcare marketing platform. In this role, you will design high-throughput data systems that process millions of behavioral events, power real-time attribution and user scoring, and enable precise targeting of healthcare professionals across 20+ channels.

You will work at the intersection of data engineering, backend systems, and marketing infrastructure, building scalable pipelines, tracking systems, and APIs that support analytics, segmentation, and campaign activation. The role also involves experimenting with AI-powered automation and agent-based workflows to improve internal operations and product capabilities.

This is a high-ownership position where you will shape architecture decisions, build critical platform components from the ground up, and help evolve our data platform as the company scales.

Responsibilities

- Architect and own data-heavy backend systems powering real-time attribution, user modeling, and scoring

- Build and evolve client-side tracking pixels (like FB pixel / Amplitude snippet / Heap autocapture) from scratch

- Set up and manage scalable ETL workflows: cleaning, merging, enriching, splitting, transforming across millions of behavioral events
Lead the build of CDP / CDXP-style data layers to support segmentation, scoring, and activation across channels

- Connect and compose systems using modern low-code / no-code tools (n8n) to accelerate ops

- Implement and maintain high-availability APIs for analytics, scoring, and campaign delivery

- Drive experimentation around AI-powered agents and automation tools that can boost internal workflows

Requirements

- System Design & Data Architecture:
Designs scalable, event-driven data platforms with strong decoupling, fault tolerance, and horizontal scalability. Builds real-time and batch pipelines using lakehouse architectures and streaming systems (Kafka / PubSub).

- Distributed Data Processing (Spark / BigQuery / Iceberg):
Expert in large-scale data processing, query optimization, and cost-efficient analytics. Deep understanding of Spark execution, partitioning, and modern table formats (Iceberg / Delta).

- Cloud & Infrastructure (GCP / Kubernetes):
Builds resilient cloud-native data platforms on GCP and Kubernetes using IaC, autoscaling, and secure access (IAM, Workload Identity).

- Python Engineering:
Develops high-performance Python services for data and ML workloads with focus on concurrency, clean architecture, and production-grade quality.

- Databases & Performance (PostgreSQL / OLTP):
Optimizes high-throughput OLTP systems through indexing, partitioning, and query analysis (EXPLAIN ANALYZE, MVCC).

- Reliability & Observability (SRE):
Implements resilient data systems with idempotency, retries, observability, SLOs, and incident management.

- AI / LLM Integration:
Integrates LLMs into production pipelines with structured outputs, caching, batching, prompt versioning, and evaluation.

- Workflow Orchestration (Airflow / Dagster):
Designs scalable DAG-based workflows with dependency management, retries, and reproducible backfills.

- CI/CD & DevOps for Data:
Implements safe deployments for data systems including schema migrations, canary releases, and data contracts.

- Ownership & Engineering Maturity:
Owns end-to-end production systems and architecture decisions while balancing delivery speed, quality, and technical debt.

Nice-to-have: 

- Experience in MarTech / AdTech platforms (e.g., DSPs, attribution systems, DMP/CDP tools)

- Deep knowledge of data matching/fingerprinting techniques (identity graphs, IP + UA resolution, hashed PII workflows)

- Strong prompt engineering skills — not just ChatGPT 101, but chaining, system prompting, tooling integration

What We Offer
- Fully remote with flexible hours (aligned with EU timezones for syncs).
- Influence on quality strategy across the entire engineering organization.
- A cross-team role with visibility into every part of the product.
- AI-first tooling.
- Claude Code licenses and cutting-edge AI development workflows. 
- A team with no bureaucracy, decisions are made fast.

We are seeking a Site Reliability Engineer; this is a fully remote job in the European time zone.

What You'll Be Doing

• Configuring and optimizing Nginx, php-fpm, PostgreSQL, ElasticSearch, Redis, RabbitMQ in production (daily routine)
• Monitoring system health with Prometheus, Victoria Metrics, Icinga, Opsgenie, Jira Service Management (daily routine)
• Managing cloud infrastructure: compute, storage, IAM, VPCs, load balancers
• Ensuring high availability and responding to incidents
• Participating in monthly on-call rotation (1 shift/month, 24h, with compensating days off)

Must Have

• Linux (RHEL) is required
• TCP/IP networking, firewalls, routing, load balancing
• Production experience with Nginx, php-fpm, PostgreSQL, ElasticSearch, Redis, RabbitMQ
• Monitoring stack: Prometheus, Victoria Metrics, Icinga, Opsgenie
• Cloud infrastructure management (any major cloud)
• English - Upper-Intermediate spoken (required)

Nice to Have

• Terraform, Ansible, Jenkins, GitLab CI
• Docker, Kubernetes, Helm
• Puppet DSL, PuppetDB
• ELK / OpenSearch stack

Conditions

• FOP contract
• Schedule: local business hours, Mon–Fri + 1 on-call shift/month (24h) with 2 compensating days off
• 24 vacation days/year

The company is a healthcare data analytics provider offering modern data infrastructure, reporting, and AI‑driven insights. Their technology focuses on improving how healthcare organizations manage, analyze, and use clinical data.

Core Solutions

•  Healthcare data platform: Consolidates structured and unstructured patient data in near real time to create a unified data foundation.

•  Longitudinal clinical data ecosystem: Enables comprehensive management and analysis of patient information.

•  Patient engagement platform: Integrates data from multiple sources to support secure communication and holistic patient care.

•  Population health management suite: Aggregates clinical and claims data, identifies at‑risk groups, and supports interventions to improve outcomes and reduce costs.

•  Care coordination and analytics tools: Provide a complete view of patient data to enhance quality, reduce costs, and support risk stratification.

•  AI and analytics services: Transform healthcare data into actionable insights for better care and operational efficiency.

Main expectations:
1.  Strong Python 3.x backend development (Flask + multithreading)
2.  Solid SQL skills and hands‑on experience with PostgreSQL
3.  Practical experience with BigQuery and Google Cloud services
4.  Containerization skills (Docker/Podman) and basic Kubernetes knowledge
5.  ETL/ELT pipeline experience and large‑scale data processing

Conditions:

• Compensation: 5500–6500 USD/month

• Contract: B2B

• Remote‑friendly

We are a global AI-powered financial intelligence company that leverages proprietary AI and large language models to deliver investment research and insights across global markets. Our platform enables financial institutions and investors to access transparent, explainable, and multilingual analysis across equities, funds, and other financial instruments.

We are building scalable, production-grade AI systems that make high-quality financial research accessible and data-driven.

We are looking for a Mid Data Science / AI Engineer to develop and deploy machine learning and AI solutions, with exposure to LLM-based systems and modern NLP applications. You will collaborate with senior engineers to build scalable and reliable AI-driven products.

Requirements:

• 3+ years of commercial experience as a Data Science / AI Engineer
• Strong Python skills and solid software engineering fundamentals
• 1+ year of experience with LLM and NLP-related tasks
• Understanding of modern deep learning architectures
• Experience with at least one deep learning framework (TensorFlow or PyTorch)
• Experience with SQL and NoSQL databases
• Good knowledge of Docker
• Strong knowledge of object-oriented programming
• Hands-on experience with RAG pipelines and vector databases
• Experience with cloud platforms (AWS, Google Cloud, Azure)

We are looking for a Senior Data Engineer to join our growing team. In this role, you will help build and scale modern data infrastructure and pipelines that power analytics, business intelligence, product insights, and operational decision-making across our omni-channel commerce platform.
 

Responsibilities

• Design, build, and maintain scalable, reliable data pipelines and infrastructure across cloud environments.
• Develop and manage ETL/ELT workflows to ingest, transform, and load data from multiple sources into data warehouses and data lakes.
• Partner closely with product, engineering, analytics, and business teams to ensure trusted, accessible, and timely data across the organization.
• Optimize data storage, modeling, and query performance to support reporting, analytics, and operational use cases.
• Build and maintain batch and real-time data processing systems using modern tools and frameworks.
• Improve data reliability and quality by implementing monitoring, validation, alerting, and observability practices.
• Support deployment, scalability, and orchestration of data workloads using containerization and workflow automation tools.
• Implement and maintain CI/CD pipelines for data workflows and infrastructure.
• Ensure data governance, privacy, and security standards are upheld across all data systems.
• Contribute to architecture decisions, code reviews, documentation, and best practices for data engineering.
• Stay current with emerging technologies and best practices in data engineering to continuously improve platform capabilities.

Requirements

• Excellent communication skills, including fluent verbal English, with the ability to explain technical concepts to non-technical stakeholders and collaborate effectively across teams.
• Proven experience as a Data Engineer, with strong hands-on experience building and managing production-grade data pipelines.
• Strong proficiency with cloud platforms such as AWS and/or Azure, including modern data services.
• Experience with Python and strong SQL skills for data transformation, automation, and analysis.
• Experience with distributed data processing and streaming technologies such as Apache Spark, Kafka, or similar tools.
• Familiarity with workflow orchestration tools such as Airflow, DBT, or equivalent.
• Experience with data warehousing and lakehouse technologies such as Snowflake, Redshift, BigQuery, Databricks, or similar platforms.
• Solid understanding of ETL/ELT design patterns, data modeling, and data architecture best practices.
• Experience with Docker, Kubernetes, or other containerization and orchestration technologies.
• Familiarity with relational and NoSQL databases such as PostgreSQL, MongoDB, or Cassandra.
• Experience with version control systems such as Git.
• Strong problem-solving skills and a proactive approach to performance optimization, troubleshooting, and continuous improvement.

Preferred Qualifications

• Experience with data governance, security, and privacy best practices in cloud environments.
• Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
• Experience supporting analytics, machine learning, or reporting use cases with well-structured data systems.
• Knowledge of BI and visualization tools such as Power BI or Tableau.
• Previous experience working in agile, cross-functional product and engineering teams.
• Experience in retail, e-commerce, omni-channel commerce, or POS platforms is a strong plus.

Why PredictSpring?

• Join a category-leading company transforming the future of retail and omni-channel commerce.
• Work with innovative technology powering seamless online and in-store customer experiences.
• Collaborate with a talented, global team in a flexible remote work environment.
• Enjoy opportunities for professional growth and career development.
• Make a direct impact on products used by some of the world’s most recognized retail brands.
• Be part of a fast-moving, collaborative, and customer-focused culture at the forefront of commerce innovation.

We are looking for an experienced Senior Data Scientist to join our team and help unlock the full value of data across our platform, products, and business operations. In this role, you will apply advanced analytics, machine learning, and statistical modeling to generate actionable insights, improve decision-making, and drive innovation across the organization.

Responsibilities

• Analyze large, complex datasets to uncover trends, patterns, and insights that support strategic business and product decisions.
• Build predictive models to forecast business outcomes, improve operational efficiency, and support decision-making across multiple functions.
• Design and implement feature engineering techniques to improve machine learning model performance and accuracy.
• Partner with cross-functional teams including product, engineering, operations, and business stakeholders to identify opportunities and translate them into scalable data science solutions.
• Clean, preprocess, and structure data from multiple sources to ensure high-quality inputs for analysis and modeling.
• Develop, test, and deploy machine learning models for use cases such as classification, regression, clustering, recommendation systems, and forecasting.
• Evaluate and optimize model performance using appropriate statistical and machine learning metrics.
• Create dashboards, reports, and visualizations that clearly communicate findings and recommendations to technical and non-technical audiences.
• Automate recurring analysis, reporting, and model workflows to improve efficiency and consistency.
• Monitor production models and continuously improve performance, reliability, and business impact over time.
• Stay current with the latest advancements in data science, machine learning, and AI, and apply relevant innovations to PredictSpring’s platform and operations.

Requirements

• Excellent communication skills, including fluent verbal English, with the ability to explain complex concepts to non-technical stakeholders and collaborate effectively across teams.
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Proven experience as a Data Scientist, with a strong background in machine learning, statistical analysis, and predictive modeling.
• Strong programming skills in Python preferred, or similar languages such as R, with hands-on experience using libraries such as Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
• Expertise in building and deploying machine learning models for classification, regression, time-series forecasting, recommendation systems, and related applications.
• Strong experience with data wrangling, feature engineering, and working with structured and unstructured datasets.
• Proficiency in SQL for querying, transforming, and analyzing structured data.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud for model deployment and data workflows.
• Experience with data visualization tools such as Power BI, Tableau, or Python visualization libraries.
• Strong analytical thinking, problem-solving ability, and a passion for applying data science to real-world business challenges.

Preferred Qualifications

• Familiarity with big data and distributed processing tools such as Apache Spark, Hadoop, or Databricks.
• Experience with deep learning frameworks such as TensorFlow, Keras, or PyTorch.
• Experience with natural language processing techniques such as text classification, sentiment analysis, or large language model applications.
• Strong foundation in statistical analysis, experimentation, and hypothesis testing.
• Experience collaborating with or supporting data engineering teams in building scalable data pipelines.
• Familiarity with version control tools such as Git.
• Strong business acumen and the ability to align technical solutions with business goals.
• Experience in retail, e-commerce, omni-channel commerce, or POS technology is a strong plus.

Why PredictSpring?

• Join a market-leading company shaping the future of omni-channel retail and modern POS.
• Work on high-impact challenges at the intersection of commerce, data, and customer experience.
• Collaborate with a talented global team in a flexible remote work environment.
• Enjoy opportunities for growth, learning, and professional development.
• Contribute to products used by some of the world’s most recognized retail and lifestyle brands.
• Be part of an innovative, collaborative, and data-driven culture focused on delivering measurable business value.

Our client is a fast-growing SaaS company in the hospitality industry. Their platform helps leading hotel brands transform front-desk teams into revenue generators through upselling training, performance analytics, and data-driven insights.

Their flagship platform is used across 2,000+ hotel properties in more than 100 countries, helping hospitality teams improve guest experiences while increasing revenue.

We are looking for an experienced Data Engineer to join a growing data team and play a key role in building and scaling data infrastructure, pipelines, and analytics capabilities.

Requirements:

• 5+ years of commercial experience in Data Engineering.
• 3+ years of hands-on experience with AWS.
• 2+ years of experience with Spark/PySpark.
• Strong Python skills.
• Experience designing and maintaining ETL/ELT pipelines.
• Experience working with large-scale, high-load data environments.
• Solid understanding of data warehousing concepts and best practices.
• Ability to work independently and take ownership of solutions.
• Proactive mindset and strong problem-solving skills.
• English C1.

Nice to Have:

• Experience with modern data orchestration tools.
• Experience with real-time data processing.
• Knowledge of CI/CD practices for data platforms.

• Experience in SaaS product companies.

   

What You'll Do:

• Design, build, and optimize scalable data pipelines.
• Develop and maintain cloud-based data infrastructure on AWS.
• Process and transform large volumes of data using Spark/PySpark.
• Ensure data quality, reliability, and performance.
• Collaborate with product, engineering, and analytics teams.
• Improve existing data architecture and propose new solutions.
• Contribute to the scalability and evolution of the company's data platform.

What we offer:

• Remote job and flexible schedule.
• Full-time position.
• Fast-paced product environment with direct impact on product quality and processes.
• Performance-review.

Waiting for your CV!

We are looking for a Senior Python Data Engineer to design and operate large-scale data pipelines that generate property attributes from imagery using our internal AI models.

This role focuses on building distributed processing systems running on Kubernetes clusters and on-prem GPU infrastructure, capable of processing massive geospatial datasets at national scale.

You will work closely with AI engineers and SaaS platform teams to build robust pipelines that transform imagery into structured property intelligence used by the insurance and financial industries.

Responsibilities

• Design and build large-scale Python processing pipelines for satellite and street-level imagery

• Develop distributed workloads running on Kubernetes clusters

• Build and maintain orchestration workflows using Airflow

• Design and manage data storage and processing workflows using PostgreSQL

• Integrate large-scale AI inference pipelines running on GPU infrastructure

• Optimize processing performance for massive imagery datasets

• Improve observability, monitoring, and reliability of production pipelines

• Collaborate with AI engineers to support large-scale computer vision inference workflows

• Integrate pipelines with AWS-based services

Requirements

• 7+ years of Python engineering experience

• Strong experience designing distributed data pipelines or large-scale data processing systems

• Experience running workloads on Kubernetes

• Hands-on experience with Airflow or similar orchestration frameworks

• Experience processing large datasets (imagery, geospatial, or other high-volume data)

• Strong understanding of ETL architectures and distributed data systems

Nice to Have

• Experience working with satellite imagery, aerial imagery, or remote sensing datasets

• Experience with GPU-based inference pipelines

• Experience working with on-prem compute clusters

• Experience with C#

Customer is one of the biggest companies on the market of home entertainment consumer electronics devices that strives to provide their clients with high-quality products and services.

This position collaborates with a geographically diverse team to develop, deliver, and maintain systems for digital subscription and transactional products across the Customer’ SVOD portfolio.

Requirements:

• 3+ years of python development (middle level, pandas, bulding APIs)
• Beginner to middle SQL
• Experience building ETLs in python
• Experience with data tools (ex.: Airflow, Grafana, Redash, Plotly, AWS Glue, AWS Athena)
• Other AWS experience
• Advanced skills in Excel
• Agile SDLC knowledge
• Detail oriented
• Data-focused
• Strong verbal/written communication, including an ability to effectively communicate with both business and technical teams
• An ability and interest in working in a fast-paced and rapidly changing environment
• Be self-driven and show ability to deliver on ambiguous projects with incomplete or dirty data

 Would be a plus:

• Understanding of basic SVOD store purchase workflows
• Background in supporting data scientists in conducting data analysis / modelling to support business decision making
• Expereince with mixpanel, mparticle, youbora and similar systems

Job responsibilities:

• Design, develop, and maintain scalable data migration pipelines to transfer data between legacy and target systems while ensuring data integrity, consistency, and minimal downtime
• Analyze source and target data models, map transformation rules, and implement ETL/ELT processes to support complex migration scenarios and business requirements
• Perform data validation, reconciliation, and quality checks to identify discrepancies, troubleshoot migration issues, and ensure accuracy of migrated datasets
• Collaborate with cross-functional teams including software engineers, QA, business analysts, and project managers to plan migration activities, resolve dependencies, and support release execution
• Optimize migration performance, automate repetitive processes, and document migration workflows, technical specifications, and operational procedures to support maintainability and future enhancements