Jobs Data Engineer

About the role
We are looking for an Agile, product-oriented Senior Analytics Engineer to drive our
modern data architecture. You will be the bridge between raw data and business value for
our digital products and online services.
In this role, speed and value are key. You will adopt an MVP (Minimum Viable Product)
mindset to launch data products quickly, iterate fast, and provide actionable insights for our
Marketing and Finance teams. You will work on our cutting-edge stack—building robust
pipelines in dbt and BigQuery, and exposing data through our next-generation semantic
layer in Omni Analytics.

What you will do
● Modern Data Modeling: Design, build, and maintain scalable dbt models in
BigQuery. Transform raw data into clean, tested, and documented data marts ready
for analysis.
● MVP Delivery: Adopt a fast-paced, product-oriented mindset. Launch Minimum
Viable Products for data reporting to give product and business stakeholders quick
answers, then iterate based on feedback.
● Semantic Layer Ownership: Implement and govern the business logic in Omni
Analytics (or similar modern BI tools), ensuring metrics are defined once and trusted
by everyone.
● Cross-Functional Analytics: Collaborate closely with Marketing and Finance
departments to understand our digital platform metrics (CAC, LTV, ROI, conversion
funnels, retention, and MAUs) and translate them into technical solutions.
● Quality & CI/CD: Apply software engineering best practices to data: version control
(Git), automated testing, and CI/CD workflows for data pipelines.

Requirements
● Experience: 6+ years in a Data Engineering, Analytics Engineering, or advanced BI
role.
● Modern Data Stack (Essential): Extensive experience with dbt and cloud data
warehouses (preferably BigQuery, though Snowflake/Redshift is valid).
● BI & Semantic Layers: Experience with Omni Analytics is highly desired.
Alternatively, strong experience in Looker (LookML), Power BI, or Tableau, with a
clear understanding of semantic layers and governed metrics.
● Domain Knowledge: Strong understanding of digital products, online services, and
high-traffic platform dynamics. Exposure to Marketing and Finance data is highly
desirable.
● Technical Skills: Advanced SQL optimization, data modeling (star schema, OBT),
and Git workflow.
● Mindset: MVP-oriented, proactive, and capable of finding innovative solutions to
challenging problems.

Tools you will work with
BigQuery · dbt · Omni Analytics · SQL · Git · Digital product analytics platforms

Why join us?
 

🌍 Remote-first & flexible work

• Fully remote working model.
• Access to an office if there is one available in your location.
• Monday–Friday, 40-hour working week.
• Flexible working hours: schedule options between 08:00 and 19:00, including a 1-hour lunch break (the only requirement is a minimum of 5 hours of overlap with the team)

🏖️ Vacation & sick leave

• 15 paid working days of vacation during the first year.
• 20 working days after 2 years.
• 25 working days after 3 years.
• 10 paid sick days per year (for childcare would also be covered), compensated at 100% of salary.
• Public holidays are days off by default.

❤️ Health & family benefits

• Medical insurance or medical insurance compensation.
• Company gifts for:
  • Birthday
  • Wedding
  • Birth of a child
• Paid lunches at the office.
• Family days etc.

📑 FOP / Individual Entrepreneur Support if needed
🔄 Paid Bench period between projects
🤝 No time-tracking software — transparent and trust-based time reporting.
🎁 Referral Program

We’re looking for someone who will take full ownership of the technical function within our Analytics department — and bring Reporting, Data Tech, and AI to a new level of maturity.

The mission is clear: transform Reporting and Data Tech into a powerful internal platform that helps the entire analytics function work faster, smarter, and more effectively.

 

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗼𝘄𝗻:

▸ Build and scale BI infrastructure from the ground up

▸ Lead and develop teams across Reporting, Data Tech, and AI

▸ Design and implement scalable Data Warehouse architecture

▸ Own ETL/ELT processes and Data Governance frameworks

▸ Drive automation across reporting and data pipelines

▸ Integrate AI/LLM tools to optimize analytical processes

▸ Act as the bridge between technical analytics and business stakeholders

▸ Drive change management and build a culture of data-driven decisions

 

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗯𝗿𝗶𝗻𝗴:

▸ 5+ years in BI / Data / Analytics

▸ Proven experience managing BI or Data teams

▸ Hands-on experience building BI functions from scratch

▸ Deep expertise in Data Warehousing and modern BI platforms

▸ SQL at expert level

▸ Strong understanding of ETL/ELT and Reporting infrastructure

▸ Practical experience with AI/LLM for process optimization

▸ Experience in change management

▸ Strong leadership, ownership mindset, and business orientation

 

𝗪𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮 𝗽𝗹𝘂𝘀:

▸ iGaming background

▸ Experience in product companies

▸ Experience working with large-scale data volumes

▸ Familiarity with modern AI tools and frameworks

 

Our Benefit Cafeteria is Packed with Goodies:

• Children Allowance
• Mental Health Support
• Sport Activities
• Language Courses
• Automotive Services
• Veterinary Services
• Home Office Setup Assistance
• Dental Services
• Books and Stationery
• Training Compensation
• And yes, even Massage!
   

Ready to Take the Leap?

Join a team where every day is an opportunity to take a RISK and come out on top. Follow us on LinkedIn and Instagram, and let us be your hub for turning ambitious ideas and extraordinary solutions into reality.

At RISK, we believe that our people are the driving force behind our success. Together, we can achieve great things and push the boundaries of what’s possible in the iGaming industry.

Шукаємо Senior / Lead Software Engineer (Data), який допоможе будувати й масштабувати production-grade data systems: пайплайни, сервіси та storage-рішення для збору, обробки, зберігання й доставки даних. Це роль для інженера з сильним software engineering mindset, який вміє працювати не лише на рівні реалізації, а й на рівні архітектури, data modeling, reliability, performance та technical leadership. Очікуємо, що людина зможе брати end-to-end ownership за критичні data workflows і впливати на стандарти команди.

Обовʼязки:

• Проєктувати, розробляти та підтримувати batch/streaming data pipelines і data services.
• Приймати технічні рішення щодо ingestion, orchestration, storage, трансформацій і delivery.
• Будувати ETL/ELT-рішення з використанням Python, Airflow, Cron.
• Інтегрувати дані з API, логів, NoSQL, IoT/embedded-джерел та внутрішніх сервісів.
• Проєктувати схеми зберігання, моделі даних і структури баз даних.
• Оптимізувати рішення з погляду performance, scalability, cost та maintainability.
• Забезпечувати data quality, observability, reliability і стабільність production-процесів.
• Планувати та реалізовувати міграції даних між платформами й форматами.
• Документувати схеми, трансформації, data lineage та ключові технічні рішення.
• Брати участь у code review, design discussions, incident resolution.
• Менторити інших інженерів і розвивати engineering standards у data-напрямку.
   

Вимоги:

• Значний досвід як Senior / Lead Software Engineer, Data Engineer або Backend Engineer у data-intensive середовищі.
• Сильний Python і досвід побудови production-ready рішень.
• Досвід роботи з Apache Airflow.
• Досвід роботи з MongoDB або іншими NoSQL-рішеннями.
• Сильне розуміння проєктування баз даних і data models: schema design, indexing, normalization/denormalization.
• Досвід роботи з великими обсягами даних, high-throughput або streaming workloads.
• Досвід із Azure Blob Storage або аналогічними object storage.
• Впевнене знання SQL та оптимізації запитів.
• Глибоке розуміння ETL/ELT, incremental processing, datalake/warehouse principles.
• Розуміння testing, observability, fault tolerance, idempotency, recovery для data systems.
• Досвід із Git, CI/CD, production deployment.
• Досвід технічного лідерства, mentoring, design review і роботи зі стейкхолдерами.
   

Буде плюсом:

• Snowflake, Elasticsearch
• Kafka або інші streaming-платформи
• Grafana / Prometheus
• Ansible або інші IaC / automation tools
• Power BI
• Досвід із cloud DWH, governance, lineage, internal data tools
  
  Надаємо бронювання*
  Офіс з повноцінним укриттям*

N-iX is looking for a Middle Data Engineer for 6 months' engagement. 

You will act as the independent hands-on engineer and the technical owner of data readiness. The role combines pipeline and query engineering with investigation of source, mapping, hierarchy, and validation issues; coordination of market and provider validation cycles; reconciliation of outputs; and delivery of technically ready datasets to downstream consumers.

The Data Engineering team builds and maintains pipelines, while mappings and configurations determine cell/model scope; Snowflake extraction jobs run SQL through Databricks, with Blob Storage, validation, Medallion transformations, Power BI review, and Gold delivery to Ekimetrics. The operating model also requires end-to-end triage across MDFA, CDF, WPP, Data Foundation, Redmill, and market stakeholders rather than treating every discrepancy as a coding defect.

Key responsibilities:

• Independently design, develop, maintain, test, deploy, and optimize ingestion, validation, transformation, aggregation, data-quality, anomaly-detection, and extraction workflows
• Develop and tune Python/PySpark packages, SQL extraction queries, Databricks jobs, ADF pipelines, Delta tables, and source-to-target contracts
• Own technical readiness for new cells and refreshes: clarify filters and expected scope, implement or update queries, review mappings/configuration, reconcile outputs, and confirm readiness for business validation
• Investigate source, NCID, taxonomy, hierarchy, mapping, naming-convention, aggregation, missing-week, outlier, and performance issues across CDF, MDFA/PFME, APIs, manual files, and specific sources
• Coordinate technical validation cycles with Product, CMIA/markets, WPP/MDFA, CDF, other data providers, and Ekimetrics; convert reported discrepancies into actionable owners and technical evidence
• Drive complex incident resolution across pipelines, data contracts, access, service principals, secrets, networking, Power BI refreshes, and downstream delivery
• Review pull requests and test evidence; guide the Junior engineer and delegate scoped engineering/support work without becoming a people manager
• Maintain architecture documentation, interface contracts, repository documentation, runbooks, deployment procedures, and support/escalation guidance

• Recommend practical automation, reliability, performance, and maintainability improvements while respecting platform standards and business-validation ownership.

   

Must-have technical competencies:

• 5–6 years’ professional engineering experience with independent production ownership
• Strong Python, PySpark, and SQL, including complex transformations, query optimization, reusable packages, debugging, tests, and reconciliation
• Strong ETL/ELT and batch-pipeline engineering across relational warehouses, APIs, object storage, and file-based ingestion
• Experience with a cloud data lake/lakehouse, Medallion patterns, schema/interface contracts, data-quality controls, orchestration, observability, and incident recovery
• Solid Git engineering practices: branching, pull requests, reviews, automated tests, deployment controls, and documentation
• Proven ability to translate business/data requirements into filters, mappings, transformations, validation rules, and operational workflows

• Stakeholder-facing problem solving: explain discrepancies, challenge incomplete requirements, establish technical owners, and drive issues to closure.

   

Nice-to-have:

• Strong preference for Azure Data Factory, Azure Databricks, Databricks Jobs/API, Delta Lake, Snowflake, Azure Blob Storage/Data Lake, Power BI, GitHub, and Azure Key Vault
• Valuable experience with Pandera or equivalent schema-validation frameworks, Streamlit, Managed Identity/service principals, Azure Communication Services, Managed VNet/Private Endpoints, and Dev/Prod release practices
• Experience with PFME/media data, syndicated sales, marketing hierarchies, MMM inputs, or multi-market data onboarding is preferred but can be learned.

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Technical Coordinator / Solution Engineer to join our dynamic team on a full-time basis.

We are looking for a hands-on technical professional who will serve as the primary on-site technical expert, supporting deployment, configuration, automation, monitoring, and troubleshooting activities within a customer environment. This role is ideal for a specialist with strong infrastructure and operational experience who enjoys working independently while collaborating closely with remote engineering and product teams.

Responsibilities

• Install, configure, and support local server-side software components.
• Support local environment setup and configuration of server and viewer-related components.
• Manage and support secure data preparation, upload, and processing workflows.
• Develop and maintain basic orchestration and automation scripts.
• Monitor technical environments and proactively troubleshoot issues.
• Investigate problems related to setup, configuration, data uploads, processing steps, and environment stability.
• Maintain setup documentation, troubleshooting records, and operational procedures.
• Communicate effectively with remote product and vendor technical teams in English.
• Escalate product-specific, environment-related, and data-quality issues to appropriate technical stakeholders.
• Work predominantly on-site at the customer location in Kyiv, depending on project needs.
• Act as the main technical representative on-site while coordinating with remote support teams.

Requirements

Required Qualifications

• Strong experience working with Windows and/or Linux server environments.
• Proven experience installing, configuring, and supporting server-side or enterprise software solutions.
• Hands-on scripting experience with Bash, PowerShell, or Python.
• Experience creating and maintaining workflow automation and orchestration scripts.
• Strong background in deployment, configuration, monitoring, and troubleshooting of technical environments.
• Understanding of secure environments, access control, credential management, and sensitive data handling.
• Ability to read technical documentation and follow setup and design instructions.
• Experience collaborating with remote technical teams and participating in English-language meetings.
• Strong analytical and troubleshooting skills.
• Ability to work independently with minimal supervision.

Nice to Have

• DevOps or Infrastructure Engineering background.
• Experience with on-premises or hybrid infrastructure environments.
• Experience working with data processing workflows.
• Familiarity with visualization platforms, viewer/server environments, or similar technical systems.
• Basic knowledge of encryption and decryption workflows.
• Experience working with APIs and integrations.
• GIS, spatial data, or 3D visualization experience.
• Customer-facing technical support, consulting, or implementation experience.

Specific Requirements

• Must be based in Kyiv or willing to work primarily from Kyiv.
• Availability for regular on-site presence at the customer location.
• Comfortable working within a sensitive and secure customer environment.
• Openness to customer-side background or security screening if required.
• Ability to act as the primary hands-on technical specialist on-site.
• Strong sense of ownership, responsibility, and communication skills.
• Ability to properly identify, prioritize, and escalate issues when necessary.

What You Should Know

• This is not a pure software development role.
• This is not a project management role.
• The primary focus is hands-on technical execution, including setup, configuration, scripting, monitoring, troubleshooting, and operational support.
• Product-specific onboarding, documentation, API guidance, and training will be provided by the product/vendor team.

Benefits

• Paid Time Off — 36 paid days per year
• Training & Development opportunities

Partial medical compensation


 

N-iX is looking for a Junior Data Engineer to join a data-focused team supporting the day-to-day operations and ongoing development of enterprise data solutions. This is a 6-month engagement with an opportunity to gain hands-on experience working with modern cloud data platforms, large-scale datasets, and production data pipelines.

You will work closely with experienced Data Engineers, contributing to pipeline maintenance, troubleshooting, onboarding new data sources, and implementing well-defined enhancements across the data platform. This role is ideal for engineers with 1-2 years of professional experience who want to deepen their expertise in data engineering and cloud technologies.

About the Role

As a Junior Data Engineer, you will help ensure the reliability and quality of data pipelines that support business-critical analytics and reporting. You'll participate in monitoring, troubleshooting, testing, and implementing data processing logic while following established engineering standards and delivery practices.

Under the guidance of senior team members, you will contribute to data ingestion processes, validation activities, technical documentation, and operational support, gaining exposure to modern technologies such as Azure, Databricks, PySpark, and Snowflake.

Responsibilities

• Monitor and support scheduled and event-driven data pipelines, data processing jobs, reporting refreshes, and data quality checks.
• Execute standard data ingestion and refresh activities from multiple sources, including APIs, CSV files, syndicated datasets, and configuration inputs.
• Implement well-defined Python, PySpark, and SQL changes, including new filters, transformations, mappings, validation rules, and minor pipeline enhancements.
• Perform schema validation, reconciliation checks, regression testing, and source-to-target data verification.
• Investigate pipeline failures, validation issues, missing data, rejected files, and reporting discrepancies.
• Troubleshoot first-line operational incidents using logs, job outputs, and monitoring tools.
• Escalate complex technical or business-data issues with clear diagnostic information and supporting evidence.
• Maintain technical documentation, deployment notes, runbooks, configuration files, and operational records.
• Follow established engineering practices, including Git workflows, pull requests, code reviews, testing, and deployment procedures.

• Support onboarding of new data sources and contribute to continuous improvement initiatives.

   

Requirements

• 1-2 years of professional experience in Data Engineering, ETL/ELT development, Analytics Engineering, or Data Support roles.
• Good working knowledge of Python and SQL.
• Understanding of data pipelines, data transformations, batch processing, and ETL/ELT concepts.
• Familiarity with relational databases and data warehouse concepts.
• Experience working with API integrations and file-based data processing (CSV, Parquet, etc.).
• Hands-on experience with Git, version control workflows, pull requests, and code reviews.
• Understanding of data quality principles, including validation, reconciliation, schema checks, and error handling.
• Ability to analyze logs, troubleshoot issues, and communicate findings clearly.
• Strong analytical and problem-solving skills.

• Intermediate or higher English level

  .

Nice to Have

• Experience with Azure Data Factory, Azure Databricks, PySpark, Snowflake, Delta Lake, Azure Data Lake Storage, or similar cloud data technologies.
• Exposure to modern data platform architectures, including Medallion (Bronze-Silver-Gold) data processing patterns.
• Familiarity with Power BI or other BI/reporting platforms.
• Knowledge of cloud environments and DevOps practices.
• Experience with data validation frameworks such as Pandera.

• Understanding of CI/CD pipelines and deployment processes.

   

What Makes You a Great Fit

• You enjoy working with data and solving technical problems.
• You are detail-oriented and take ownership of the quality of your work.
• You are comfortable working within established processes while continuously learning new technologies.
• You communicate clearly and collaborate effectively with technical and non-technical stakeholders.
• You are proactive, curious, and eager to grow your data engineering skills in a production environment.

N-iX is looking for a Middle Data engineer for 6 months' engagement. 

You will act as the independent hands-on engineer and the technical owner of data readiness. The role combines pipeline and query engineering with investigation of source, mapping, hierarchy, and validation issues; coordination of market and provider validation cycles; reconciliation of outputs; and delivery of technically ready datasets to downstream consumers.

The Data Engineering team builds and maintains pipelines, while mappings and configurations determine cell/model scope; Snowflake extraction jobs run SQL through Databricks, with Blob Storage, validation, Medallion transformations, Power BI review, and Gold delivery to Ekimetrics. The operating model also requires end-to-end triage across MDFA, CDF, WPP, Data Foundation, Redmill, and market stakeholders rather than treating every discrepancy as a coding defect.

Key responsibilities:

• Independently design, develop, maintain, test, deploy, and optimize ingestion, validation, transformation, aggregation, data-quality, anomaly-detection, and extraction workflows
• Develop and tune Python/PySpark packages, SQL extraction queries, Databricks jobs, ADF pipelines, Delta tables, and source-to-target contracts
• Own technical readiness for new cells and refreshes: clarify filters and expected scope, implement or update queries, review mappings/configuration, reconcile outputs, and confirm readiness for business validation
• Investigate source, NCID, taxonomy, hierarchy, mapping, naming-convention, aggregation, missing-week, outlier, and performance issues across CDF, MDFA/PFME, APIs, manual files, and specific sources
• Coordinate technical validation cycles with Product, CMIA/markets, WPP/MDFA, CDF, other data providers, and Ekimetrics; convert reported discrepancies into actionable owners and technical evidence
• Drive complex incident resolution across pipelines, data contracts, access, service principals, secrets, networking, Power BI refreshes, and downstream delivery
• Review pull requests and test evidence; guide the Junior engineer and delegate scoped engineering/support work without becoming a people manager
• Maintain architecture documentation, interface contracts, repository documentation, runbooks, deployment procedures, and support/escalation guidance
• Recommend practical automation, reliability, performance, and maintainability improvements while respecting platform standards and business-validation ownership.

Must-have technical competencies:

• 5–6 years’ professional engineering experience with independent production ownership
• Strong Python, PySpark, and SQL, including complex transformations, query optimization, reusable packages, debugging, tests, and reconciliation
• Strong ETL/ELT and batch-pipeline engineering across relational warehouses, APIs, object storage, and file-based ingestion
• Experience with a cloud data lake/lakehouse, Medallion patterns, schema/interface contracts, data-quality controls, orchestration, observability, and incident recovery
• Solid Git engineering practices: branching, pull requests, reviews, automated tests, deployment controls, and documentation
• Proven ability to translate business/data requirements into filters, mappings, transformations, validation rules, and operational workflows
• Stakeholder-facing problem solving: explain discrepancies, challenge incomplete requirements, establish technical owners, and drive issues to closure.

Nice-to-have:

• Strong preference for Azure Data Factory, Azure Databricks, Databricks Jobs/API, Delta Lake, Snowflake, Azure Blob Storage/Data Lake, Power BI, GitHub, and Azure Key Vault
• Valuable experience with Pandera or equivalent schema-validation frameworks, Streamlit, Managed Identity/service principals, Azure Communication Services, Managed VNet/Private Endpoints, and Dev/Prod release practices
• Experience with PFME/media data, syndicated sales, marketing hierarchies, MMM inputs, or multi-market data onboarding is preferred but can be learned.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

We are looking for a Lead Platform Engineer to lead the team behind the Equals 5 web platform, the Laravel + Vue application that pharma marketing teams use to run and measure their campaigns. This is a technical management role: you lead a small team of developers and a QA engineer, and you keep writing code yourself. You propose how a feature should be built, write it down as an FRD, agree it with the CTO, and then take it through review, release and whatever it does in production. You report to the CTO and work day to day with our product lead, who decides what we build and why.

Requirements

General

• 2+ years leading engineers as a tech lead, team lead or hands-on engineering manager: task breakdown, code review, mentoring, 1:1s
• 5+ years building and shipping web products as a full-stack engineer
• Experience carrying a whole product in production: releases, incidents, data correctness
• Comfortable in a fully remote team with hours overlapping EU timezones
• English B2 or above. Most of our communication is written and async

Technical

• Laravel in depth: Eloquent, queues, events, service providers, testing. We run Laravel 12 on PHP 8.3, with queues on Redis
• Vue: the SPA is Vue 2.7 with the Options API, Vuex and Vue Router, and there is no Composition API anywhere in it. Either Vue 2 or Vue 3 experience counts, as long as you can work inside an existing codebase and improve it. The rest of the frontend is dated too (Vite 2, bootstrap-vue 2, chart.js 2, vee-validate 2), so this is a frontend platform question rather than a framework upgrade. What we do about it, and when, is one of the first calls we will ask you to make with us
• PostgreSQL beyond CRUD: indexing, query plans, migrations against live tables. Production is Cloud SQL 14 while local and CI run 17, so a major-version upgrade is on the table
• Real SQL ability plus hands-on work with an analytical store: ClickHouse, BigQuery, Snowflake or similar. Our largest tables run to 1.5 billion rows, and Apache Druid is still in the hot path behind several controllers, so there are three analytical stores to hold in your head
• Writing FRDs and keeping technical documentation current: requirements, architecture decisions, API contracts, data models. We do not have this discipline today, and establishing it is a core part of the role rather than a bonus
• GCP at the service-design level: Cloud Run, Cloud SQL, GCS. You answer for what you deploy
• Docker and CI. GitHub Actions is primary, on a mix of self-hosted and GitHub-hosted runners, and a legacy Bamboo pipeline survives as a manual fallback for production deploys
• Automated testing as a habit. A mature Cypress E2E suite of 135 specs gates every pull request; PHPUnit coverage across 819 PHP files and 206 migrations is uneven, and there are no frontend unit tests at all. Closing that gap is part of the job
• Production debugging with Sentry and Laravel and nginx logs. Observability is thin today and improving it is yours to drive

Soft

• You lead in practice: you split work for other people, review their code, unblock them, and say no when that is the right answer
• You write clearly in English: FRDs, decision records, review comments
• Analytical: you find the root cause before you propose a fix
• Task management: your board reflects what is actually happening, and you raise risks while there is still time to act on them
• Care with healthcare-professional data: access, consent, PII
   

AI baseline:

AI-assisted development is how we ship, not something we are piloting. Concretely, what we expect from day one:

• Daily use of AI coding agents (Claude Code, Cursor, Codex or similar): required, not “I tried it once”
• 50%+ of the code you ship is drafted by agents and reviewed by you line by line
• Able to run 2–4 agents in parallel on isolated branches or worktrees and merge the results yourself
• You maintain agent context for your projects: CLAUDE.md / AGENTS.md, custom commands, rules files. At least 1 such artifact you wrote yourself and keep current
• You automate at least 1 recurring routine per month with an agent: reports, migrations, release checks, test generation
• You can tell when the agent is wrong and throw the output away. Whatever ships, you are the engineer on the hook for it — we ask about this in the interview
   

Nice to have:

• Ad-tech or marketing analytics background: impressions, clicks, attribution windows, audience matching
• Healthcare or pharma domain, especially consent and PII handling
• Infrastructure as code with Pulumi or Terraform. Our infrastructure is in Pulumi, in a separate repository
• Experience migrating a Vue 2 codebase to Vue 3
• TypeScript. There is none in the frontend today. Whether we introduce it, where we start, and whether the cost is worth paying is a call for this role to make
• Apache Druid
• BigQuery and ClickHouse cost and performance tuning
   

Responsibilities:

• Lead a small team of 2–4 developers plus QA: task breakdown, code review, mentoring, input into hiring
• Stay hands-on: you write code yourself as well as distributing it
• Design how features are built (data model, API, UI, tests, rollout), write the FRD, and take it through review with the CTO before the work starts
• Keep technical documentation current after the feature ships: architecture, API contracts, data models
• Work directly with the product lead on weekly priorities
• Query and shape campaign data across PostgreSQL, BigQuery, ClickHouse and Druid, and keep dashboards fast
• Own releases and reliability: deploys across four production VMs with an NFS-shared release directory and automatic rollback, monitoring, alerting, incident response
• Look after the per-PR preview environments, where every pull request gets its own database clone and a routed environment
• Work with healthcare-professional data under our access, consent and PII rules
• Report to the CTO and agree architecture and technical standards with them
   

Tech stack:

PHP 8.3 / Laravel 12 · Vue 2.7, Vuex, Vite · PostgreSQL 14 in production (17 locally and in CI), Redis · ClickHouse, BigQuery, Apache Druid · GCP, Docker, GitHub Actions · Sentry · PHPUnit, Cypress · Claude Code

What we offer:
- 18 days PTO
- 5 days sick leave
- 11 US national holidays
- 50% of course price (if relevant to job)
- Laptop
 

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Engineer to join our growing Data & Analytics practice and build modern data solutions for our customers on AWS.

This is a highly hands-on engineering role. You’ll work across different customer environments and data challenges, from modernizing legacy pipelines and building lakehouse platforms to optimizing analytical workloads and owning smaller data platforms end-to-end.

You won’t be limited to one stack or one long-running product. One project may involve Spark and Apache Iceberg, another Redshift performance and data modeling, and another Kafka, OpenSearch, or a new data platform built from ingestion through analytics.

We’re looking for someone who can take ownership of a data engineering problem, make sound technical decisions, and deliver independently without needing constant direction.

📍 Work location: Ukraine, remote

If you are interested in this opportunity, please submit your CV in English.

Responsibilities

• Design, build, and optimize production data pipelines and data platforms on AWS.
• Build scalable data processing workloads using Spark/PySpark, Python, and SQL.
• Build and modernize data lakes and lakehouse architectures, working with technologies such as Amazon S3, Apache Iceberg, AWS Glue, and related data services.
• Design, troubleshoot, and optimize analytical workloads and data warehouses, including Amazon Redshift and other OLAP platforms.
• Modernize legacy and inefficient data workloads, including moving existing ETL/ELT processing to scalable distributed data architectures.
• Work with streaming and event-driven data pipelines using technologies such as Kafka, Kinesis Data Firehose, and related services.
• Investigate real production data problems, from performance bottlenecks and data modeling issues to upstream/downstream dependencies, and recommend practical solutions.
• Take ownership of customer projects end-to-end, from understanding the technical problem through implementation, testing, and delivery.
• Work directly with customers when needed to clarify requirements, explain technical decisions, and recommend solutions based on your expertise.
• Build data foundations for analytics and ML workloads, collaborating with Data Scientists, DevOps, and MLOps engineers.
• Contribute to dashboards and analytics when required using QuickSight or other BI tools.

• Apply practical engineering practices around data quality, testing, CI/CD, security, governance, and infrastructure automation.

   

Requirements

• Strong production experience in Data Engineering, with the ability to independently own and deliver data projects.
• Deep hands-on experience with Apache Spark / PySpark and distributed data processing.
• Strong Python and SQL skills.
• Hands-on experience building data solutions on AWS.
• Strong understanding of data lakes, lakehouse architectures, ETL/ELT, data modeling, and data processing at scale.
• Experience with analytical databases and data warehouses. Strong Amazon Redshift experience is highly valuable; deep experience with platforms such as Snowflake, Synapse, or similar OLAP technologies is also relevant.
• Understanding of streaming architectures and technologies such as Kafka or Amazon Kinesis.
• Ability to troubleshoot existing data systems, understand how data flows across upstream and downstream components, and improve performance and reliability.
• Ability to work autonomously, make pragmatic technical decisions, and take responsibility for delivery.

• Good communication skills and the ability to discuss technical problems and solutions directly with customers in English.

   

Nice to have

• Experience with Apache Iceberg or other modern open table formats and lakehouse technologies.
• Amazon OpenSearch Service / Elasticsearch experience.
• Experience with BI and visualization tools such as Amazon QuickSight, Tableau, or Power BI.
• Terraform and Infrastructure as Code.
• CI/CD practices for data pipelines, including data testing and validation.
• Experience preparing data platforms for ML workloads using services such as Amazon SageMaker.
• Exposure to Amazon Bedrock, GenAI, or agentic development.

• Experience with Databricks, Snowflake, ClickHouse, or other modern data platforms

   

What Success Looks Like in Year One

• Independently owns assigned data projects end-to-end, from understanding the problem and making technical decisions to implementation, troubleshooting, testing and successful delivery, with minimal supervision.
• Has built strong practical expertise across the team’s core data stack and can confidently work across different customer environments, including Spark, Python/SQL, data lakes/lakehouses, analytical and NoSQL databases, streaming and other technologies used by the team.
• Can investigate unfamiliar data problems independently, identify performance or data-modeling issues, understand upstream/downstream dependencies, and recommend pragmatic solutions to customers.

• Continuously expands technical depth, keeps up with modern Data technologies and patterns, and can quickly learn new technologies as customer projects and the Data practice evolve.

   

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program — enjoy cooperation with your colleagues and get a bonus
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes

• Soft skills training

   

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognising the value you bring to our

Position Overview

We are looking for a Junior SRE / DevOps Engineer to join our infrastructure team and help support Kubernetes environments, CI/CD pipelines, application deployments, and production systems.

This role is a good fit for someone who already has basic hands-on experience with Linux, containers, Kubernetes, and CI/CD and wants to grow into an SRE / DevOps position.

Responsibilities

• Support Kubernetes-based environments.
• Assist with application deployments and CI/CD pipelines.
• Work with Jenkins pipelines.
• Support Java application build processes using Maven (mvn).
• Help troubleshoot deployment and infrastructure issues.
• Monitor applications and infrastructure.
• Work with logs, metrics, and alerts.
• Assist with infrastructure automation.
• Maintain technical documentation.
• Work with developers and senior DevOps/SRE engineers on production issues and improvements.

Required Skills

• Basic Linux administration skills.
• Basic understanding of Kubernetes.
• Understanding of Docker and containers.
• Basic experience with Jenkins or another CI/CD system.
• Basic understanding of Maven (mvn) and Java application build processes.
• Understanding of Git.
• Basic networking knowledge: DNS, TCP/IP, HTTP/HTTPS.
• Ability to read logs and troubleshoot basic application or infrastructure issues.
• Basic scripting skills with Bash or Python.
• Willingness to learn and work with production infrastructure.

Nice to Have

• Experience with Helm.
• Experience with Prometheus and Grafana.
• Basic knowledge of cloud platforms such as AWS, Azure, or GCP.
• Experience with Terraform or other Infrastructure-as-Code tools.
• Knowledge of Dremio.
• Basic understanding of Apache Arrow.
• Experience with PostgreSQL or object storage.
• Understanding of SRE concepts such as monitoring, alerting, availability, and incident response.

What We Expect

We do not expect a Junior engineer to know everything.

The important things are strong fundamentals, willingness to learn, and the ability to investigate problems instead of only following predefined instructions.

The engineer will work together with more experienced team members and gradually take more responsibility for CI/CD, Kubernetes, monitoring, and production infrastructure.

ORIL is seeking a highly experienced and autonomous Data Architect / Lead Data Engineer to lead a 2–3 month strategic project focusing on a comprehensive audit and redesign of our mission-critical, AI-driven real estate data platform.

The ideal candidate is a "rockstar" data expert with strong data architecture, data discovery, and data engineering expertise. You will take complete ownership of evaluating our current data layer, determining the target future state (Desired State), and defining a concrete execution roadmap for the engineering team.

About the Project

Our client is a US-based innovator providing a centralized data platform for multi-family real estate. The platform aggregates complex real estate datasets from various external integrations and sources to feed real-time BI analytics (Metabase), power automated operational insights, and drive an intelligent Text-to-SQL AI chatbot.

What are we looking for?

• 6+ years of experience as a Data Architect, Lead Data Engineer, or Senior Database Engineer;
• Proven track record of conducting end-to-end Data Discovery and Audits of complex, multi-source data architectures;
• Deep technical expertise in PostgreSQL and relational database design, including advanced query optimization, indexing, schema design, and data modeling;
• Hands-on experience with Data Quality, Data Governance, and Data Integrity frameworks (monitoring freshness, volume anomaly, schema drift, semantic metadata validation);
• Extensive background in evaluating and architecting ETL/ELT data pipelines and integrations (APIs, web scraping, CDC pipelines);
• Strong understanding of structuring metadata, schemas, and semantic layers for AI/LLM integrations (Text-to-SQL) and BI reporting tools (Metabase);
• Ability to translate complex business needs into high-level technical vision, concrete architecture blueprints, and resource estimates;
• English level: Fluent or Advanced (C1) with strong stakeholder-facing communication skills for direct interactions with US client leadership.

What will you do?

•  Evaluate the current state of all data collected across multiple integrations — how data is ingested, processed, stored, and presented across the platform.
• Analyze schema design, data freshness, bottleneck risks, and overall data health to identify system vulnerabilities and inaccuracies.
• Design a clean, scalable, and future-proof target data architecture optimized for fast querying, BI reporting (Metabase), and AI/LLM ingestion (Text-to-SQL).
• Build a detailed step-by-step execution plan (Roadmap) specifying required engineering steps, timelines, and resource allocations needed to reach the desired state.
• Present audit findings and strategic recommendations directly to the US client leadership and product team.

We Offer:

• Project-based contract engagement (2–3 months) with flexible Part-time or Full-time capacity;
• Potential for long-term cooperation upon successful project delivery;
• Competitive, market-leading compensation;
• Full accounting support;
• Fully remote work flexibility;
• Agile, transparent engineering culture.

Application Process:

1. Initial Screening Call with Recruiter.
2. Technical & Architecture Deep Dive with CTO.
3. Culture Index (CI) Test.

N-iX is looking for a Junior Data Engineer to join a data-focused team supporting the day-to-day operations and ongoing development of enterprise data solutions. This is a 6-month engagement with an opportunity to gain hands-on experience working with modern cloud data platforms, large-scale datasets, and production data pipelines.

You will work closely with experienced Data Engineers, contributing to pipeline maintenance, troubleshooting, onboarding new data sources, and implementing well-defined enhancements across the data platform. This role is ideal for engineers with 1-2 years of professional experience who want to deepen their expertise in data engineering and cloud technologies.

About the Role

As a Junior Data Engineer, you will help ensure the reliability and quality of data pipelines that support business-critical analytics and reporting. You'll participate in monitoring, troubleshooting, testing, and implementing data processing logic while following established engineering standards and delivery practices.

Under the guidance of senior team members, you will contribute to data ingestion processes, validation activities, technical documentation, and operational support, gaining exposure to modern technologies such as Azure, Databricks, PySpark, and Snowflake.

Responsibilities

• Monitor and support scheduled and event-driven data pipelines, data processing jobs, reporting refreshes, and data quality checks.
• Execute standard data ingestion and refresh activities from multiple sources, including APIs, CSV files, syndicated datasets, and configuration inputs.
• Implement well-defined Python, PySpark, and SQL changes, including new filters, transformations, mappings, validation rules, and minor pipeline enhancements.
• Perform schema validation, reconciliation checks, regression testing, and source-to-target data verification.
• Investigate pipeline failures, validation issues, missing data, rejected files, and reporting discrepancies.
• Troubleshoot first-line operational incidents using logs, job outputs, and monitoring tools.
• Escalate complex technical or business-data issues with clear diagnostic information and supporting evidence.
• Maintain technical documentation, deployment notes, runbooks, configuration files, and operational records.
• Follow established engineering practices, including Git workflows, pull requests, code reviews, testing, and deployment procedures.
• Support onboarding of new data sources and contribute to continuous improvement initiatives.

Requirements

Must Have

• 1-2 years of professional experience in Data Engineering, ETL/ELT development, Analytics Engineering, or Data Support roles.
• Good working knowledge of Python and SQL.
• Understanding of data pipelines, data transformations, batch processing, and ETL/ELT concepts.
• Familiarity with relational databases and data warehouse concepts.
• Experience working with API integrations and file-based data processing (CSV, Parquet, etc.).
• Hands-on experience with Git, version control workflows, pull requests, and code reviews.
• Understanding of data quality principles, including validation, reconciliation, schema checks, and error handling.
• Ability to analyze logs, troubleshoot issues, and communicate findings clearly.
• Strong analytical and problem-solving skills.
• Intermediate or higher English level.

Nice to Have

• Experience with Azure Data Factory, Azure Databricks, PySpark, Snowflake, Delta Lake, Azure Data Lake Storage, or similar cloud data technologies.
• Exposure to modern data platform architectures, including Medallion (Bronze-Silver-Gold) data processing patterns.
• Familiarity with Power BI or other BI/reporting platforms.
• Knowledge of cloud environments and DevOps practices.
• Experience with data validation frameworks such as Pandera.
• Understanding of CI/CD pipelines and deployment processes.

What Makes You a Great Fit

• You enjoy working with data and solving technical problems.
• You are detail-oriented and take ownership of the quality of your work.
• You are comfortable working within established processes while continuously learning new technologies.
• You communicate clearly and collaborate effectively with technical and non-technical stakeholders.
• You are proactive, curious, and eager to grow your data engineering skills in a production environment.

ZentixSoft is looking for a Senior Data Solutions Consultant / Solution Architect! 🚀

Format: Project-Based / Hourly Engagement (Remote). 

💡 Why ZentixSoft?

💸 Fair Compensation: Transparent hourly/project-based rates for expert consulting hours.

⚖️ Work-Life Balance: Flexible scheduling tailored around your primary availability.

🦾 Trust & Freedom: Pure professional autonomy with zero micromanagement and no time-trackers.

🎁 Culture & Community: A collaborative environment where your architectural decisions directly drive business outcomes.

🧩 Project & Role Overview:

You will support our technical and presales teams during the discovery and solution design phase of a complex product data management initiative (handling legacy data sources, supplier onboarding, taxonomy, attribute mapping, and multi-system synchronization).

 Discovery & Workshops: Participate in client-facing discovery sessions and technical workshops to unpack legacy architecture and business constraints.

 Architecture Design: Analyze product-data flows, identify systems of record, and distinguish between integration/ETL challenges versus semantic data-mapping issues.

 Roadmap & Strategy: Define realistic target architecture options, phased implementation roadmaps, and potential pilot use cases.

 PIM / MDM & AI Integration: Evaluate approaches for supplier data onboarding, taxonomy harmonization, and assess where AI/LLM automation adds real value vs. deterministic rules/governance.

 Proposal Support: Help formulate solution proposals, architecture diagrams, and technical recommendations for business and technical stakeholders.

🎓 Our Perfect Match (Requirements):

 Domain Experience: 5+ years of hands-on experience in data architecture, system integrations, and solution design.

 PIM / MDM / Product Data Expertise: Strong background in Product Information Management (PIM), Master Data Management (MDM), product taxonomy, attribute mapping, and supplier onboarding.

 Platform Exposure: Experience with platforms such as Akeneo, Salsify, Stibo Systems, Informatica, Contentserv, Pimcore, or inriver (or similar product catalog ecosystems).

 Technical Integration & Data Quality: Familiarity with ERP/PIM/e-commerce integrations, product data standards (e.g., BMEcat), data deduplication, golden-record concepts, and legacy SQL/Access environments.

 Modern Tech Evaluation: Understanding of AI/LLM-assisted data classification and automated mapping techniques.

 Consulting & Communication: Proven experience leading technical workshops with clients, with Fluent / Confident English for client-facing communication.

📍 Engagement Details:

⏳ Format: Project-based / Hourly consulting that can be easily combined with your full time occupation.

📍 Location: 100% Remote.

We look forward to reviewing your profile:

The Mission
Are you a cloud architect who finds beauty in a perfectly orchestrated pipeline? Do you believe that “manual” is a four-letter word? We are looking for a Senior SRE specialized in BI Ecosystems to join our mission-critical Infrastructure team.
In this role, you won’t just “maintain” systems; you will be the master weaver connecting our advanced Data Analytics tools with a rock-solid, automated GCP/AWS backbone. You will bridge the gap between traditional Business Intelligence and modern Cloud Engineering, ensuring our data flows are fast, our visualizations are seamless, and our infrastructure is invisible.

What You Will Do
1. Master the Orchestration (GCP & GKE)
You will take full ownership of our Google Kubernetes Engine (GKE) clusters. This isn’t just
about pods and services; you’ll manage advanced networking (VPC Service Controls, Shared VPC) and high-availability node configurations to ensure our data platforms never skip a beat.
2. Advance Data Architectures (Lakehouse)
You will design and support a unified Data Lakehouse architecture by integrating BigQuery and Cloud Storage. Your mission is to break down silos, ensuring that our raw data and analytical models coexist in a single, high-performance ecosystem. You will implement robust governance and security policies that ensure data quality and accessibility, providing a “single source of truth” that is both scalable and cost-effective
3. Evangelize Infrastructure as Code (IaC)
Our goal is 100% immutability. You will use Terraform to automate the entire lifecycle of our
GCP resources—from Compute Engine and Cloud Run to Cloud SQL—ensuring consistency
across Dev, Staging, and Production.
4. Empower DataViz Excellence
You will be the enabler for our Data Analysts. You’ll deploy, optimize, and scale Qlik Sense,
Tableau, or Similars environments on GCP, handling everything from SSO/IAM authentication to high-performance connectivity with our backend data sources.
5. Hybrid Cloud & Legacy Modernization
Complexity is our playground. You will manage critical AWS resources (EC2, S3) and ensure
seamless interoperability with GCP. Simultaneously, you will lead the charge in migrating legacy monolithic systems into modern, microservices-based architectures.
6. Data Engineering Collaboration
Working side-by-side with Data Engineers, you’ll help build and maintain robust pipelines using Cloud Dataflow, Cloud Composer (Airflow), and BigQuery. You ensure that the “plumbing” of our data world is leak-proof and high-performing.
7. Reliability, Security & FinOps
● CI/CD: Design sophisticated pipelines (GitLab CI/GitHub Actions) integrating DevSecOps best practices.
● Observability: Implement deep monitoring using Google Cloud Operations Suite and Grafana.
● FinOps: Be the guardian of our budget, identifying and eliminating cloud waste through
smart optimization.

Who You Are (Requirements)
● Cloud Expert: Proven experience managing complex environments in Google Cloud Platform (GCP) and AWS.
● Data Architect: Deep understanding of Data Lakehouse models and governance.
● Kubernetes Pro: Deep knowledge of GKE, including advanced networking and security policies.
● Terraform Ninja: Ability to write modular, scalable, and secure IaC.
● BI Infrastructure Savvy: Experience deploying and tuning enterprise visualization tools (Tableau, Qlik, or Power BI).
● Automation Mindset: Strong scripting skills (Python, Bash, or Go) and mastery of CI/CD engines.
● Data-Driven: Familiarity with Big Data tools like Airflow, BigQuery, and Dataflow.
● Security-First: Solid understanding of encryption, IAM roles, and network security standards.

Why Join Us?
● Impact: You are not a cog in the machine; you are building the machine.
● Tech Stack: Work with the latest tools in the Cloud and Data world (Lakehouse, GKE).
● Culture: We value technical excellence, proactive problem-solving, and a “fail fast, learn
faster” mentality.
● Growth: We invest in your certifications and continuous learning. Ready to redefine how BI meets SRE? If you are a builder who loves solving the puzzle of high-scale data infrastructure, we want to meet you.

Apply now and let’s build the future of data infrastructure together!

About the role 

We're looking for a Senior Data Architect to join a long-term engagement with an enterprise client. You'll take ownership of an existing, complex Azure data platform and quickly assess, stabilise and modernise it. You'll also design the data layer behind GenAI, RAG and Agentic AI applications. 

This is not a hands-on LLM or AI-agent development role. What matters is that you have designed, in production, how enterprise data and knowledge is ingested, prepared, stored, governed and made available for retrieval and AI consumption. 

What you'll do 

• Own the architecture of an existing Azure data platform: assess it fast, separate tactical fixes from long-term target architecture, and move both forward in parallel. 
• Deliver implementation-ready architecture: detailed documentation, diagrams and recommendations that engineering teams can act on. You'll be expected to show meaningful output within the first 1–2 weeks. 
• Design the data and knowledge layer for RAG and Agentic AI applications: document processing, OCR and text extraction, metadata, chunking, embeddings, and vector search and retrieval. 
• Design scalable, metadata- and configuration-driven ingestion for many databases, APIs and file-based sources. 
• Work with structured and genuinely unstructured data (PDFs, scans, images, emails, free text). 
• Make technology-neutral decisions about when Synapse, Databricks, ADF, Azure SQL or a Lakehouse are justified, and when they only add complexity. 
• Improve performance, scalability, maintainability and data quality, including batch, incremental processing and incremental refresh. 

Must-have 

• Strong, recent experience as an enterprise Azure Data Architect, with clear architecture ownership. 
• At least one real production project (not a POC) where you designed or owned the data architecture behind a RAG, GenAI or Agentic AI application. 
• Azure Cosmos DB, in depth (essential): data modelling, partition-key strategy, indexing, RU/throughput and cost optimisation, scaling, consistency, Change Feed, and knowing when Cosmos DB is the right choice and when it isn't. 
• Azure Synapse Analytics, current and in depth: Pipelines, Spark, Serverless SQL, Dedicated SQL Pools, and how Synapse compares with Azure SQL, ADF, ADLS, Databricks and Fabric. 
• Azure Databricks, Azure SQL MI / SQL Server / T-SQL, Azure Data Lake / Lakehouse architecture, and Azure Functions. 
• Strong data modelling: fact and dimension tables, star schemas, and optimising analytical models. 
• Proven experience taking over a problematic legacy data platform and modernising it. 
• Ability to work independently with incomplete information and turn analysis into concrete deliverables quickly. 
• Fluent English (C1 preferred) and strong communication with both technical teams and business stakeholders. 

What we offer 

• A 12-month full-time contract with a long-term perspective 
• A high-impact architecture role at the intersection of enterprise data platforms and GenAI 
• Fully remote work 
• A short hiring process: an intro call and a 1-hour technical interview 

How to apply 

Please send a CV tailored to this role. The client screens CVs strictly and won't review a generic CV or a list of keywords. For each relevant project, please include: 

• start and end dates, context, and your personal role and decisions 
• the Azure services you used (Cosmos DB, Synapse, Databricks and so on) and how you used them 
• for your RAG/GenAI project: what the application did, what you personally owned, how the data was prepared for retrieval, which storage and vector-search technologies you used, whether Cosmos DB was part of it, and whether it was in production 

Please also include: 

• your expected hourly rate (USD) 
• your English level 
• years of experience 
• your country of residence and your citizenship 
• your availability date 

Shortlisted candidates will be asked to complete a skills matrix.