Jobs Data Engineer

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.

We seek a Senior Data Engineer – Databricks & dbt to build and maintain scalable data pipelines and transformation solutions on Azure Databricks.
This hands-on role focuses on Databricks, dbt, SQL, Python/PySpark, Lakehouse architecture, data quality, and reliable end-to-end data delivery.

Main Responsibilities:

• Build, test and maintain data pipelines on Databricks using SQL, Python and PySpark.
• Build Python integrations for source APIs, custom ingestion, reusable components and internal tooling.
• Apply Bronze/Silver/Gold modelling principles.
• Develop and maintain the dbt transformation layer, including models, sources, snapshots, tests, contracts, data-quality controls and documentation.
• Translate business logic into modular and performant staging, intermediate and mart models.
• Monitor data quality, source freshness and failed runs.
• Orchestrate workflows using Databricks Workflows/Lakeflow and dbt.
• Work with Unity Catalog, access controls, ownership, lineage and cost governance.
• Collaborate with the Data Architect and Technical Lead.
• Contribute to platform standards, patterns, tooling and peer code reviews.
• Work within Git-based CI/CD processes across DEV, UAT and PROD.
• Support integrations with Azure SQL and, where relevant, Azure Data Factory.
• Explore AI use cases on Databricks, including MCP-based interfaces and Databricks Apps.
• Ideally help coach junior Data Engineers.

Key Requirements:

• At least 2 years’ hands-on data or analytics engineering experience.
• Production experience with Azure Databricks.
• Demonstrable delivery experience with dbt Core or dbt Cloud.
• Experience delivering work items end to end within a structured delivery team.
• Advanced SQL and analytical/dimensional data modelling.
• Python and PySpark, including modular, testable API integrations.
• Databricks Lakehouse architecture and Bronze/Silver/Gold patterns.
• Databricks Asset Bundles.
• Databricks Workflows/Lakeflow.
• Strong dbt knowledge: incremental models, snapshots, Jinja, macros, tests, contracts, exposures and documentation.
• Git, CI/CD and DevOps practices.
• Understanding of Unity Catalog, governance and data ownership.
• English is required.

Valuable additional experience:

• Technical-lead or senior-contributor experience.
• Running and troubleshooting dbt on Databricks.
• Azure Data Factory.
• Databricks AI capabilities, MCP servers or assistant-based interfaces.
• Databricks Apps.
• Power BI and semantic data modelling.
• Coaching junior engineers

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

We are looking for a Senior Data Engineer to own the data platform of a fast-growing startup, from architecture all the way to production. The company helps investors and other teams find and research companies, so reliable, fresh and well-structured data is at the heart of the product. Today the platform processes data one job at a time in containers. You will lead its move to running many jobs at once, with event-driven data intake and orchestrated batch workflows. Your daily work will combine Python, Airflow, dbt, GCP and PostgreSQL with large-scale web scraping from many sources. You will also solve hard data problems, such as matching company and people records across sources and using LLMs to pull structured data out of text. This role suits someone who likes building from zero, enjoys owning a wide area, works well on their own and is comfortable in a fully remote team with no fixed working hours.

Details:

• Schedule: Full time
• Location: Remote – EU 
• Start: ASAP
• Duration: Long-term
• English: Fluent
• Type of collaboration: B2B Contract

About the project:
The client is a startup building a data platform that helps investors and other teams find and research companies. The platform collects data from many sources, cleans it, links related records and turns it into useful insights about companies. As the product grows, the current one-at-a-time processing needs to become a faster, event-driven system that handles many jobs at once. The Senior Data Engineer will lead this change and own the whole platform, from design to day-to-day running. The team is small, so engineers own a wide area and have a direct impact on the product. The company works fully remotely, and you don't need to overlap with anyone's working hours.

You have:

• Experience building a production data platform with many data sources from zero, including its architecture, deployment and day-to-day running
• Production experience with workflow orchestration tools (Airflow, Dagster, Prefect or Temporal)
• Hands-on experience with dbt and Google Cloud Platform (GCP)
• Strong Python skills, including asynchronous HTTP and concurrent processing
• Hands-on web scraping experience (Scrapy, Playwright, Selenium or Beautiful Soup)
• Strong PostgreSQL skills: data modeling, query optimization and reliable incremental loading
• Experience with event-driven architecture, including retries, failure recovery and dead-letter queues
• Comfort working fully remotely and on your own, with no fixed hours that overlap with the team
• Fluent English

Nice to have:

• Experience in startups or small engineering teams where you owned a wide area
• Entity resolution experience, especially matching and reconciling company and people records across sources
• Experience using LLMs to extract structured data or enrich data
• Familiarity with BigQuery and Dataform
• Background in company, investment or B2B data platforms

What to do:

• Own the data platform from start to finish, from architecture to production and day-to-day running
• Lead the move from one-at-a-time container processing to concurrent, event-driven data intake and orchestrated batch workflows
• Build and maintain data pipelines with Python, Airflow, dbt and GCP
• Build and scale web scraping that runs across many sources at once, handling rate limits and failures
• Design PostgreSQL data models, optimize queries, and set up reliable incremental loads, idempotent upserts and backfills of historical data
• Build solid failure recovery, retries and dead-letter handling for event-driven data intake
• Match and reconcile company and people records across different data sources
• Use LLMs to extract and enrich structured data where it helps

ORIL is seeking a highly experienced and autonomous Data Architect / Lead Data Engineer to lead a 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 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.

MINT is the leader in Advertising Resource Management (ARM) software, driving marketing transformation in the age of agentic AI. At the core of our innovation is an open enterprise platform that deploys autonomous AI agents to augment human teams across the marketing and advertising value chain, connecting marketing performance with enterprise growth and real business impact. 

Thanks to a diverse team of over 300 professionals worldwide, we integrate technology, data, and agentic intelligence to redefine how global brands and agencies plan, execute, and optimize their advertising investments. 

With the recent acquisitions of technology leaders Redmill Solutions and info³, MINT has further expanded its unified ecosystem, completing the long-awaited connection of media planning, buying, and execution within one intelligent environment. 

The Role 

As a Senior Data Engineer, you will be responsible for designing, building, and optimizing data architectures, pipelines, and ETL processes.  

You will leverage technologies such as Snowflake, DBT, and Airflow to develop and manage scalable data solutions that support efficient analytics and business decision-making. 

This is a consultancy engagement, not an employment contract. The selected consultant will work independently, bringing their specialized knowledge and experience to our projects. 

Responsibilities  

• Develop and maintain a robust Data Warehouse using Snowflake and DBT 
• Design, build, and optimize ETL processes and data pipelines 
• Implement and manage workflows using Airflow, Kubernetes, and AWS services 
• Deploy and manage data warehouses to support scalable analytics 
• Ensure data quality, security, and performance in all data operations 

This role will be a great fit if you have 

• Advanced proficiency in Python and SQL for data analysis and organization 
• Solid understanding of DBT for ETL purposes 
• Practical experience with Airflow for workflow management 
• Experience working with Snowflake infrastructure 
• Background in designing scalable data architectures and pipelines 
• Familiarity with relational database systems and, ideally, exposure to Apache Druid 
• 4-5 years of relevant experience, with a professional level between strong middle and senior 
• A Master’s degree in Computer Science or a related field (preferred) 

Interview Process 

Candidates can expect the following steps in our interview process: 

• HR Video Interview (30 minutes): A virtual interview with HR to provide an overview of the company and the role, as well as to gain insight into your experience and background.   
• Technical Interview with Tech Leadership team (1 hour): A virtual interview with the Head of Engineering and team leads focused on your technical expertise, past experience, and problem-solving approach. Expect deep dives into current challenges such as scaling data pipelines, improving developer velocity, managing service migrations, and optimizing cloud costs. We'll also explore specific use cases like handling data spikes, enhancing observability in distributed systems, and refactoring legacy components. 
• Interview with CPTO (30 minutes): A high-level conversation with our CPTO to align on technical vision, product thinking, and leadership style. This is an opportunity to discuss your approach to driving impact across teams, balancing speed with quality, and contributing to long-term strategy and innovation. 
• Offer: after completing the interview process, successful candidates will receive an offer. 

MINT is committed to a diverse and inclusive workplace. MINT AI is an equal opportunity employer and does not discriminate on the basis of age, race, religion, color, national origin, gender, gender identity, gender expression, sexual orientation, immigration status, medical condition, protected veteran status, disability, genetic information, political views or activity, or other legally protected status.  

Kraków, Poland. Hybrid — 2 days per week from the office.
Own Caliente's data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

Kraków, Poland. Hybrid — 2 days per week from the office. 

What you'll be doing

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.
• Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.
• Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.
• Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.
• Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.
• Monitor ingestion health, data anomalies, and cloud storage/compute spend.

Must-have

• Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.
• Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.
• CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.
• Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.
• Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.
• Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.
• Cloud object storage — S3 or GCS, plus storage tiering and archival.
• Python and an orchestrator — Airflow or Dagster, as tools.

Nice to have

• Fintech, iGaming, or another regulated, audit-heavy environment.
• Cost monitoring / FinOps for storage and compute spend.
• Hudi specifically; Dagster specifically.
• Immutability / WORM regulatory retention.

About the team
We are a focused engineering team of 5 to 9 people at a multi-product tech SaaS company. We work across personalization, recommendation, fraud detection, and some GenAI, through real-time, near-real-time, and batch pipelines. The team is a blend of Python backend engineers, ML engineers, QA (manual and automation), a data engineer, and data scientists. Our shared mandate is to take experimentation from our data scientists and turn it into
robust, production-grade systems, either as standalone services or integrated into wider platforms.

The role
You will build the data pipelines that power our experimentation and production systems, the backbone behind fraud detection, recommendations, and personalization. You will work primarily with Kafka (sometimes Flink), Spark on Databricks, and Airflow, modelling and optimizing data for both experimentation and production serving, and partnering closely with data scientists and backend engineers on data contracts and reliability.

Responsibilities
• Build and operate batch and streaming data pipelines (structured & unstructured data)
• Work primarily with Kafka, Spark on Databricks, and Airflow
• Model and optimize data for both experimentation and production serving
• Partner with data scientists and backend engineers on data contracts and reliability
• Own pipeline quality: testing, monitoring, and performance
• Take part in the teams on-call rotation for the pipelines you build and operate

Requirements
• 3+ years of data engineering or software engineering for data

• Strong Python, including PySpark
• Kafka and stream processing
• Databricks experience
• Airflow or similar orchestration tools
• Strong SQL and data modeling, plus solid relational database skills
• Software engineering mindset
• Fluent spoken English, as you’ll work with an international team and all interviews will be conducted in English.

Nice to have
• Flink

• ML Feature stores
• MLOps and ML pipeline exposure
• Docker and Kubernetes
• SaaS or high-scale product domain experience

Why join us
• Work on high-scale, high-throughput systems
• Broad range of technologies, stacks, and problems
• Contribute to turning experimentation into production systems
• A strong engineering culture that values technical depth and ownership
• Flexibility through hybrid and remote working
• A distributed team across Dubai and Europe
 

You will get

• A position in a product development company with a dynamic environment and several concurrent projects;
• Continuous self-improvement and growth, including budget for certifications and courses;
• Competitive salary plus financial bonuses;
• Medical insurance coverage;
• Company prepaid AI agent;
• Wellbeing package: online-yoga classes, Yakaboo, BetterMe App: Health Coaching, BetterMe App: Mental Health;
• Corporate events and fun team-building activities.
• Remote-first culture
   

Interview Stages

1. HR Interview (30-45 minutes) — Initial conversation to discuss your experience, career goals, and cultural fit.
2. Technical Interview (1 hour) — In-depth technical interview covering relevant skills.
3. Final Interview (1,5 hour) — A comprehensive discussion with the team, focusing on role-specific competencies and alignment with company values.
4. Reference check & Job Offer

We are looking for a Mid Data Engineer in Kraków, Poland who will own the company data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

Domain: Regulated iGaming / wallet & ledger data. Audit-heavy: regulators, finance and analytics all consume the same tables. Millions of financial events per day, terabyte-plus scale.
 

What you'll be doing:

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.
• Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.
• Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.
• Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.
• Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.
• Monitor ingestion health, data anomalies, and cloud storage/compute spend.
  
  Must-have:
• 3 years' experience in hands-on delivery within that architecture — pipelines, ingestion, models, monitoring
• Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.
• Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.
• CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.
• Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.
• Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.
• Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.
• Cloud object storage — S3 or GCS, plus storage tiering and archival.

• Python and an orchestrator — Airflow or Dagster, as tools.

   

  Location & work model:

• Kraków, Poland. Hybrid — 2 days per week from the office.

We are looking for a Senior Data Engineer to join a strategic marketing technology (Martech) transformation initiative for a large enterprise organization. The project focuses on modernizing the client's marketing data platform, rebuilding data pipelines, and improving marketing attribution across multiple business brands.

As part of a dedicated data engineering team, you will help build a scalable, cloud-native data platform on Google Cloud Platform (GCP), enabling better visibility into marketing performance and supporting data-driven decision making. This is engagement through the end of 2026, working closely with the client's Data Engineering, Product, and Martech teams.

Responsibilities:

• Design, develop, and maintain scalable data pipelines on Google Cloud Platform (GCP).
• Build and optimize data models in BigQuery to support analytics and marketing reporting.
• Rebuild legacy marketing data pipelines following modern engineering best practices and enterprise standards.
• Contribute to the development of a canonical enterprise data model and semantic data layer.
• Support the implementation and adoption of DataHub as the enterprise data catalog.
• Develop automated CI/CD pipelines for data engineering workflows and enforce coding and data quality standards.
• Ensure proper data isolation between multiple business brands while enabling consolidated reporting where appropriate.
• Collaborate closely with Product Managers, Martech specialists, Analytics teams, and other data engineers to deliver business value.
• Optimize data processing performance, reliability, monitoring, and cost efficiency.

Required Qualifications:

• 5+ years of experience as a Data Engineer.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Advanced experience with BigQuery.
• Strong SQL and Python development skills.
• Experience designing and implementing modern ELT/ETL pipelines.
• Solid understanding of dimensional modeling, semantic modeling, and enterprise data architecture.
• Experience building production-grade CI/CD pipelines for data platforms.
• Familiarity with Git and modern software engineering best practices.
• Strong communication skills and ability to work within cross-functional teams.
• Upper-Intermediate or higher English.

Nice to Have:

• Experience with marketing technology (Martech) platforms and marketing attribution.
• Hands-on experience integrating: Google Ads, Google Analytics (GA4), Search Ads 360 (SA360), Campaign Manager 360, DV360
• Experience with DataHub or similar data catalog solutions.
• Experience implementing semantic layers for analytics platforms.
• Understanding of multi-brand or multi-tenant data architectures.
• Experience modernizing legacy data platforms and rebuilding pipelines from scratch.

About the Project

We are looking for an experienced Data Engineer (Azure Databricks / PySpark) to join a long-term project for an Austrian client operating in the logistics industry.

The project focuses on developing and enhancing a modern cloud-based data platform, enabling seamless data integration, improving data quality, and supporting data-driven business decisions.

You will work within an Azure-based Lakehouse environment, building reliable data pipelines, integrating multiple data sources, and supporting analytical solutions powered by Databricks and Power BI.

This is an excellent opportunity to contribute to a large-scale data ecosystem, work with modern cloud technologies, and participate in a stable, long-term international project.
 

Responsibilities

• Design, develop, and maintain data integrations and pipelines using Azure Databricks, PySpark, and Python.
• Implement data integrations according to predefined interface specifications and architectural guidelines.
• Perform data profiling, implement data quality rules, and ensure data consistency and reliability.
• Develop, activate, monitor, and optimize data flows from source systems into data products.
• Support the development and integration of Power BI semantic models, datasets, reports, and dashboards.
• Collaborate with data architects, analysts, and business stakeholders to deliver scalable data solutions.
• Contribute to the development and continuous improvement of the Lakehouse architecture.
• Optimize data processing performance, reliability, and maintainability.
• Participate in Agile development processes, including planning, implementation, testing, and deployment.
• Support CI/CD and infrastructure automation practices within the Azure ecosystem.
   

Requirements

• Proven commercial experience in Data Engineering.
• Hands-on experience with Azure Databricks and PySpark.
• Strong programming skills in Python and SQL.
• Experience designing and implementing ETL/ELT pipelines and data integrations.
• Good understanding of modern data platform architectures, particularly Lakehouse.
• Experience with data profiling, data validation, and data quality management.
• Familiarity with Power BI, including data modeling, semantic models, and/or DAX.
• Experience working with cloud-based data platforms, preferably Microsoft Azure.
• Understanding of Git-based development workflows and Agile methodologies.
• Good communication skills and the ability to work in an international team.
• English and/or German language proficiency (B1–B2 or higher, depending on project requirements).
   

Nice to Have

• Experience with Azure Synapse Analytics.
• Knowledge of the DQX Framework or similar data quality frameworks.
• Experience with Terraform and Infrastructure as Code (IaC).
• Familiarity with Azure DevOps, GitHub, and CI/CD pipelines.
• Understanding of Data Mesh principles and API-based integrations.
• Experience optimizing Power BI data models, reports, and DAX queries.
• Previous experience in logistics, transportation, or supply chain projects.
   

What We Offer

• Long-term cooperation on an international project with expected annual extensions.
• Fully remote working format.
• Opportunity to work with modern Azure Data Engineering technologies.
• Collaboration with an international team and an Austrian client.
• Involvement in developing and improving a modern enterprise data platform.
• Full-time engagement with a flexible start date.

Are you passionate about building reliable, scalable, and highly available production systems? Do you enjoy solving complex engineering challenges through automation, observability, and software engineering?
Optimove is looking for a Senior Site Reliability Engineer (SRE) to join our global SRE organization. In this role, you'll partner with engineering teams globally to improve the reliability, scalability, and operational excellence of our cloud platform.
As a Senior SRE, you'll lead engineering-driven reliability initiatives by building automation, improving deployment processes, advancing observability, and driving operational excellence across our production environment.

Responsibilities
 

• Reliability Engineering – Design and implement solutions that improve the reliability, availability, and scalability of our production platform while proactively identifying and eliminating operational risks.
• Automation & Platform Engineering – Build internal tools and automation that eliminate manual operational work, improve engineering productivity, and streamline production workflows.
• Observability – Improve monitoring, alerting, dashboards, and production visibility while driving initiatives that reduce alert fatigue, improve incident detection, and strengthen operational insights.
• Production Rollouts – Design, improve, and support production deployment processes using modern release strategies such as Canary, Blue/Green, and Feature Flags to ensure safe and reliable releases.
• Production Reliability & Incident Response – Lead the investigation and resolution of critical production incidents, perform root cause analysis, and implement long-term preventive improvements that improve platform resilience and reduce recurring issues.
• On-Call & Operational Excellence – Participate in the team's on-call rotation, ensuring reliable production operations. Respond to critical incidents, collaborate with engineering teams during service disruptions, and drive engineering improvements, automation, and operational best practices that reduce operational toil and prevent future incidents.
• Cloud & Infrastructure – Design, maintain, and improve our Kubernetes-based cloud platform, CI/CD pipelines, and production infrastructure running on GCP and AWS.
• Engineering Partnership – Collaborate closely with Software Engineers, DevOps, DBAs, and Product teams to improve reliability across the software development lifecycle and promote SRE best practices.
   

Requirements
 

• 5+ years of experience in Site Reliability Engineering, Platform Engineering, DevOps, or Infrastructure Engineering.
• Hands-on experience operating Kubernetes in production environments.
• Strong experience working with public cloud platforms (GCP or AWS).
• Strong programming and scripting skills (Python preferred; Go or Bash are a plus).
• Experience designing and building automation and internal engineering tools.
• Experience working with CI/CD pipelines and modern deployment methodologies.
• Hands-on experience with observability platforms such as Datadog, Prometheus, or Grafana.
• Strong understanding of Linux, networking, distributed systems, and cloud-native architectures.
• Excellent troubleshooting, debugging, and root cause analysis skills.
• Strong communication skills and experience collaborating with globally distributed engineering teams.
   

Advantages
 

• Experience with Infrastructure as Code (Terraform, Ansible, etc.).
• Experience with messaging and distributed technologies such as Kafka, Pub/Sub, or Redis.
• Experience supporting modern deployment strategies such as Canary, Blue/Green, or Feature Flags.
• Familiarity with OpenTelemetry and modern observability tooling.
• Understanding of Site Reliability Engineering principles, including SLIs, SLOs, and error budgets.
• Experience working in large-scale SaaS production environments.
• Relevant cloud or Kubernetes certifications (GCP, AWS, CKA, CKAD).
  
  Why Join Us?
  Join a team where Site Reliability Engineering is treated as an engineering discipline—not just an operations function. You'll work on large-scale cloud infrastructure serving enterprise customers while building automation, improving observability, and enabling engineering teams to deliver software safely and reliably.
• You'll collaborate with a global SRE organization using modern cloud-native technologies, Kubernetes, distributed systems, and large-scale SaaS workloads. You'll have the opportunity to lead impactful reliability initiatives, influence engineering best practices, and solve complex production challenges that directly improve the experience of both customers and engineering teams.

ʼAbout the role

You will join the team that owns the Data Lake: ingestion, storage, transformation and the platform other teams build their pipelines on. The domain is broad on purpose — one week you are tuning a Spark job, the next you are designing a CDC ingestion pattern, writing Terraform, or shaping how a business case becomes a data product.

Your future responsibilities:

• Design and build data pipelines end-to-end: ingestion from heterogeneous sources → cleaning, transformation, enrichment, normalisation/denormalisation → publication — with tests, monitoring and production ownership
• Make and defend the architectural calls in your slice: file/table format, partitioning, incremental vs. full reload, latency/cost trade-offs, failure semantics
• Translate business cases into requirements, architecture and code — and push back when the requirement is the problem
• Find both the fast path for an experiment and the stable path for production, and know which one you are on
• Continuously look for what in our processing should be simplified, automated or deleted

Your skills and experience:

Must have — breadth across the stack:

• Python as a working language for data engineering (not just scripting)
• SQL fluently: window functions, complex joins, reading and reasoning about execution plans
• Spark / PySpark: transformations, aggregations, window functions, UDFs — and enough understanding of shuffle, skew and partitioning to open a Spark UI and explain why a job is slow
• AWS data stack: S3, Glue, Athena, Lambda, Step Functions, IAM (equivalent depth in GCP/Azure is fine)
• Lakehouse fundamentals: Parquet, Iceberg (or Delta/Hudi) — partitioning, compaction, schema evolution, snapshots
• Orchestration: Airflow (or Dagster / Prefect / Step Functions)
• Docker, and comfort running workloads on Kubernetes
• Git + CI/CD, and exposure to IaC (Terraform / Terragrunt)
• Streaming and CDC concepts: Kafka, delivery semantics, late and out-of-order data, idempotent writes
• Debugging as a first-class skill: logs, metrics, a failed job at 9am, and a calm root-cause narrative afterwards
• English: you can read and write technical documentation
• Middle: 2+ years in a dedicated data engineering role. Solid across most of the above, genuinely strong in two or three areas.
• Senior: 5+ years designing, building and operating Data Lake / DWH solutions. Plus: you design things other engineers build on, you mentor, and you can carry a technical decision across team boundaries.

Must have — AI tooling:

• Hands-on experience with coding agents (Claude Code, Codex or similar) on production work: decomposing a task, giving it the right context, iterating to a result you would sign off on
• Critical review of generated code. You do not merge what you cannot explain line by line — correctness, cost and compliance stay yours regardless of who typed it
• Willingness to work outside your strongest area, using AI to get productive in an unfamiliar tool rather than routing the task elsewhere
• Bonus: RAG, embeddings, MCP servers, agent workflows, evals — we use all of these in the platform (metadata search, SQL generation over the lake, data classification, quality checks)

Nice to have:

• A second language beyond Python (Scala, Java, Go, Rust)
• Owning IaC properly: Terraform / Terragrunt modules, not just editing existing ones
• dbt / SQLMesh, data contracts, lineage and catalog tooling
• Analytical databases: ClickHouse, Redshift, BigQuery, Snowflake
• Data modelling background (Kimball, Data Vault) and experience with regulated / audited data environments
• Experience building complex analytical systems end-to-end

We offer what matters most to you:

• Competitive salary: we guarantee a stable income and annual bonuses for your personal contribution. Additionally, we have a referral program with rewards for bringing in new colleagues to Raiffeisen Bank. 
• Social package: official employment, 28 days of paid leave, additional paternity leave, and financial assistance for parents with newborns. 
• Comfortable working conditions: possibility of a hybrid work format, offices equipped with shelters and generators, modern equipment. 
• Wellbeing program: all employees have access to medical insurance from the first working day; consultations with a psychologist, nutritionist, or lawyer; discount programs for sports and purchases; family days for children and adults; in-office massages. 
• Training and development: access to over 130 online training resources; corporate training programs in CX, Data, IT security, leadership, agile, сorporate library. 
• Great team: our colleagues form a community where curiosity, talent, and innovation are welcome. We support each other, learn together, and grow. You can find like-minded individuals in over 15 professional communities, reading clubs, or sports clubs. 
• Career opportunities: we encourage advancement within the bank across functions. 
• Innovations and technologies: Infrastructure: AWS, Kubernetes, Docker, GitHub, GitHub actions, ArgoCD, Prometheus, Victoria, Vault, OpenTelemetry, ElasticSearch, Crossplain, Grafana. Languages: Java (main), Python (data), Go (infra, security), Swift (IOS), Kotlin (Android). Data stores: Sql-Oracle, PgSql, MsSql, Sybase. Data management: Kafka, AirFlow, Spark, Flink. AI: building expertise and actively integrating into processes. 
• Support program for defenders: we maintain jobs and pay average wages to mobilized individuals. For veterans, we have a support program and develop the Bank’s veterans community. We work on increasing awareness among leaders and teams about the return of veterans to civilian life. Raiffeisen Bank has been recognized as one of the best employers for veterans by Forbes. 

Why Raiffeisen Bank? 

• Our main value is people, and we support and recognize them, educate them and involve them in changes. Join Raif’s team because for us YOU matter! 
• One of the largest lenders to the economy and agricultural business among private banks. 
• Recognized as the best employer by EY, Forbes, Robota.ua, Stud-Point, Delo.UA, NV, Odgers, UAFIC. 
• The largest humanitarian aid donor among banks (Ukrainian Red Cross, UNITED24, Superhumans, СМІЛИВІ). 
• One of the largest IT product teams among the country’s banks. 
• One of the largest taxpayers in Ukraine; 6 billion UAH were paid in taxes in 2025. 

Opportunities for Everyone: 

• Raif is guided by principles focused on human development and the well-being of 5,000 employees and over 2.5 million clients. 
• We uphold the principles of diversity, equity, and inclusion. 
• We are open to hiring veterans and people with disabilities and are ready to adapt the work environment to meet your specific needs. 
• We collaborate with students and experienced professionals alike, creating opportunities for growth at every stage of a career 

You matter at Raif! 

Want to learn more? — Follow us on social media:

Facebook, Instagram, LinkedIn

Responsibilities

• Design and improve perception algorithms for autonomous driving applications.
• Develop solutions for 3D object detection, semantic segmentation, object tracking, and drivable area estimation.
• Create and optimize automated data labeling pipelines to improve dataset quality while reducing manual annotation efforts.
• Implement reliable multi-object tracking algorithms operating in 3D environments.
• Train, evaluate, and optimize large-scale deep learning models using extensive datasets and high-performance computing infrastructure.
• Validate developed solutions through testing in real autonomous vehicle platforms.

Requirements

• Master's or PhD degree in Computer Science, Robotics, Artificial Intelligence, or a related technical field (or equivalent industry experience).
• Strong practical experience with deep learning for at least one sensor type, such as cameras, LiDAR, or radar.
• Experience working with modern neural network architectures, including Transformer-based models.
• Solid programming skills in Python and C++.
• Ability to work collaboratively in a fast-paced engineering environment with a strong willingness to learn.

Nice to Have

• Previous experience in autonomous driving, ADAS, robotics, or computer vision.
• Familiarity with deployment and optimization frameworks such as TensorRT, ONNX Runtime, or LibTorch.
• Experience contributing to medium or large-scale software systems.
• Understanding of performance optimization for machine learning inference.

What We Offer

• Opportunity to work on advanced AI technologies shaping the future of mobility.
• Highly technical environment with minimal bureaucracy and fast decision-making.
• Flexible working hours and hybrid work options.
• Competitive compensation package.
• Long-term growth opportunities within an innovative engineering team.
• Equity or long-term incentive program.

US Company is searching for a Data & Automation Engineer to join an AI-first product company building an advanced communications platform powered by Large Language Models (LLMs). This is an exciting opportunity to work across the full technology stack, designing scalable backend services, modern user interfaces, cloud infrastructure, and AI-powered features in close collaboration with a US-based engineering team. Full-time, official contract, remote work, and flexible schedule (covering US business hours till 2pm, CST).
 

Brief project description:
The product is an AI-driven communications platform that enables businesses to automate and enhance customer interactions through voice, messaging, and intelligent workflows powered by modern LLM technologies.

Responsibilities:

• Build and maintain data workflows using PostgreSQL and AWS.
• Automate processes with Lambda, EventBridge, and APIs.
• Develop and maintain reports, dashboards, and data integrations.
• Troubleshoot data issues and ensure data quality and reliability..

Required skills (must have):

• Strong PostgreSQL/SQL skills: 5+ years.
• Hands-on AWS experience, especially Lambda and EventBridge.
• Handling Webhooks ~> API Gateway ~> Lambda
• Event/Service Bus ~> Lambda ~> Outgoing API
• Terraform
• Experience with REST APIs and reporting/BI tools.
• Strong analytical, troubleshooting, and communication skills.
• 3+ years in data engineering, analytics, or a related technical role.
• Excellent English and ability to work independently.

Would be considered as a plus:

• Python or Node.js/TypeScript.
• Experience with additional AWS services.
• Experience with BI tools such as Power BI, Tableau, or Metabase.
• Experience with ETL/ELT, dbt, or Infrastructure-as-Code.

Work conditions:

• 100% remote work.
• Full-time (40 hours per week).
• Official contract.
• Flexible schedule with required overlap with US business hours (till 2pm, CST).

Interviewing / Hiring process:
Step 1 - preliminary interview (main questions) - 45 mins
Step 2 - internal technical interview - 60 mins
Step 3 - technical interview with Team Leader - 60 mins

We're looking for a Senior SysOps Engineer to join our friendly and professional team.

The platform is a high-traffic web service handling tens of millions of requests per day, with a data pipeline processing 50M+ user events daily that feeds both customer-facing analytics and billing. It runs on self-managed infrastructure across two datacenters, with a mix of long-lived legacy components and an active modernization program. 
 

Engagement: full-time (ideally) or part-time (20+ hours/week).
Location: UA/EU, remote.
 

Required Skills:

• 5+ years of experience as a SysOps Engineer / Infrastructure Engineer (or similar role).
• Strong expertise with OS: FreeBSD (preferred), Linux (Debian, Ubuntu), Jail, Docker, and ZFS optimization.
• Database experience: MariaDB/MySQL with master-master, master-slave replication, MyISAM, InnoDB, GTID.
• Experience with Redis & Sentinel: setup, replication, and failover strategies.
• Experience with Web-servers: Caddy, Lighttpd, Nginx.
• Knowledge of ElasticSearch high-load clusters (replication, scaling, performance tuning).
• Experience with Cloudflare (DNS management, caching, security configurations).
• Experience with Supervisord, Beanstalkd, RabbitMQ.
• Networking & security expertise: Strong understanding of network protocols, packet filtering (iptables, ipfw, pf).
• Proficiency in scripting & automation: Bash, Python, PHP (or similar).
• Experience with PowerDNS and DNS management strategies.
• English - B2 and higher (for effective communication with stakeholders).

Responsibilities:

• Maintain, manage, and improve the existing infrastructure and platform.
• Troubleshoot and resolve issues with critical platform components (ElasticSearch, Redis, FreeBSD, database replication, etc.).
• Maintain and update incident runbooks for the on-call teams.
• Automate system tasks and reduce manual repetitive work.
• Document and optimize deployment processes.
• Work on high-availability solutions for branded domains, database failover, and scaling improvements.

Will Be a Plus:

• Experience with high-load multi-source database replication, Orchestrator, and ProxySQL.
• Knowledge of high-load infrastructure solutions and best practices.

What we offer:
 

• Official B2B contract with a Canadian Company;
• Compensation for medical insurance (for our employees based in UA);
• Flexible working hours (start at 8.00-11.00 o'clock);
• Weekends according to the Ukrainian/Local calendar;
• Engaging projects with opportunities for career growth;
• Certifications, Udemy courses, Claude;
• Corporate events, team-building activities,
• Gifts and recognition from the company;
• A friendly, open, and supportive team culture without excessive bureaucracy.

Join the Dataforest Team!

We are actively expanding our team and looking for strong new talent to join our ranks. If you are a Data Engineer seeking a friendly team, a healthy working environment, and a flexible schedule — you have found the right place to send your CV!

You'll be a great fit if you have:

- 2+ years of experience with Python;
- 2+ years of experience as a Data Engineer;
- Experience with Pandas;
- Experience with SQL DB / NoSQL (Redis, Mongo, Elasticsearch) / BigQuery;
- Familiarity with Amazon Web Services;
- Experience with Kafka;
- Knowledge of data algorithms and data structures is a MUST;
- Working with high volume tables 10m+.

What you'll do:

- Create ETL pipelines and data management solutions (API, Integration logic);
- Different data processing algorithms;
- Involvement in creation of forecasting, recommendation, and classification models.

Would be a plus:

- Experience with Spark (pyspark);
- Experience with Airflow;
- Experience in statistics;
- Knowledge of DS and Machine learning algorithms.

We ensure your growth with:

- 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.