Jobs Data Engineer

We are looking for a Senior Data Engineer Consultant to join an AI consulting engagement focused on LLM architecture, RAG, data strategy, and AI platform advisory.
 

This is an advisory role rather than a full-time implementation position. The consultant will work with AI architects, ML engineers, MLOps specialists, and the client’s engineering team to assess the current data landscape, review proposed solutions, and provide practical technical recommendations.
 

Responsibilities
 

Data Strategy
 

• Review the existing enterprise data landscape and available data sources.
   
• Assess data readiness for foundation model training, fine-tuning, evaluation, and RAG.
   
• Recommend data acquisition, prioritization, and preparation approaches.
   
• Advise on multilingual data preparation strategies.
   

Data Pipelines
 

• Review the current ETL/ELT architecture and data processing workflows.
   
• Recommend pipeline designs for model training, inference, and evaluation.
   
• Evaluate orchestration, dataset versioning, and data traceability approaches.
   
• Assess pipeline scalability, reliability, maintainability, and performance.
   

RAG Data Engineering
 

• Review document ingestion architecture for enterprise knowledge bases.
   
• Recommend parsing, chunking, metadata enrichment, embedding, and indexing approaches.
   
• Evaluate vector database and search integration strategies.
   
• Advise on incremental updates, synchronization, access-aware retrieval, and retrieval optimization.
   

Data Governance and Quality
 

• Review data lifecycle management and data governance practices.
   
• Assess PII handling, data masking, retention policies, access control, lineage, and auditability.
   
• Recommend data validation and quality-control frameworks.
   
• Review training and evaluation datasets.
   
• Advise on monitoring data freshness, consistency, completeness, and data drift.
   

Documentation and Consulting
 

• Prepare technical recommendations, architecture assessments, and implementation guidelines.
   
• Review technical documentation, diagrams, and proposed designs.
   
• Participate in architecture reviews, design discussions, and technical workshops.
   
• Conduct knowledge-transfer sessions with the client’s engineering team.
   

Technology Stack
 

Python, SQL, Apache Airflow, Apache Kafka, Hadoop, Apache Superset, n8n, ETL/ELT, RAG Pipelines, Vector Databases, Data Governance, Cloud (AWS, Azure or GCP).
 

Preferred Domain Experience
 

Experience in banking, fintech, financial services, or another highly regulated domain is highly desirable.
 

The ideal candidate should understand domain-specific requirements related to:
 

• sensitive and confidential data;
   
• PII protection and data masking;
   
• role-based access control;
   
• data retention and auditability;
   
• regulatory and compliance requirements;
   
• secure use of enterprise data in AI and LLM solutions.
   

Engagement Details
 

• Engagement model: Part-time consulting.
   
• Workload: Flexible and non-uniform, depending on the current project phase.
   
• Expected activities: Architecture reviews, workshops, document reviews, technical assessments, and advisory sessions.
   
• Duration: Approximately 6 months.
   
• Project type: AI/LLM consulting engagement.
   
• Client interaction: Direct collaboration with the client’s engineering and architecture teams.
   
• Availability: The consultant should be able to join scheduled sessions and provide asynchronous reviews between meetings.
   

We are seeking a proactive Middle Big Data Engineer to join our team. In this role, you will be responsible for designing, developing, and maintaining scalable data pipelines and reporting-ready data assets within Palantir Foundry. The ideal candidate will possess a robust background in cloud technologies, data architecture, and a passion for solving complex data challenges.

You will collaborate with a cross-functional team and stakeholders to translate business requirements into scalable data solutions, enabling reporting, improving data quality, and driving data transformation initiatives.

About the Client:

Our client is a leading global healthcare organization, driving innovation in pharmaceuticals, biotechnology, and patient-centric solutions. They develop advanced data and analytics solutions that help global organizations turn complex data into intuitive digital tools, enabling teams to make informed decisions in their daily operations and long-term strategy. Their projects span modern operational applications, interactive dashboards, and advanced analytics platforms designed to deliver actionable insights across multiple business domains.

Key Responsibilities:

• Collaborate with cross-functional teams to gather business requirements and design, implement, and maintain scalable data pipelines in Palantir Foundry, ensuring end-to-end data integrity and optimized workflows
• Design and maintain data ingestion, transformation, and orchestration pipelines using Palantir Foundry, Python, and PySpark
• Develop reporting-ready datasets and data models to support downstream analytics and Power BI reporting
• Develop, optimize, and maintain efficient ETL/ELT processes to collect, process, and integrate data from multiple sources, ensuring timely and accurate data delivery
• Implement data quality validation, monitoring, and health checks to ensure reliability and consistency of data assets
• Monitor pipeline performance, identify bottlenecks, troubleshoot production issues, and continuously improve scalability and processing efficiency
• Plan and prioritize work independently, communicate progress transparently, and proactively manage changing priorities while proposing practical solutions when requirements evolve
• Maintain clear technical documentation and promote automation and continuous improvement across data engineering processes
• Stay current with emerging technologies and industry best practices, incorporating innovative approaches into data engineering solutions

Required Qualifications:

• 3+ years of experience in data engineering, preferably within the pharmaceutical or life sciences industry
• Strong proficiency in Python and PySpark
• Proficiency with big data technologies (e.g., Apache Hadoop, Spark, Kafka, BigQuery, etc.)
• Hands-on experience with cloud services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow)
• Expertise in data modeling, data warehousing, and ETL/ELT concepts
• Hands-on experience with database systems (e.g., PostgreSQL, MySQL, NoSQL, etc.)
• Proficiency in containerization technologies (e.g., Docker, Kubernetes)
• Effective problem-solving and analytical skills, coupled with excellent communication and collaboration abilities

Nice to have:

• Experience with market research data
• Familiarity with Veeva CRM, Reltio, SAP, and/or Palantir Foundry
• Background in pharma or healthcare
• Experience with automation / pipeline optimization

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

N-iX is looking for a Senior Big Data engineer to join our team.

Our client is a leading global healthcare organization, driving innovation in pharmaceuticals, biotechnology, and patient-centric solutions.

We develop advanced data and analytics solutions that help global organizations turn complex data into intuitive digital tools, enabling teams to make informed decisions in their daily operations and long-term strategy. Our projects span modern operational applications, interactive dashboards, and advanced analytics platforms designed to deliver actionable insights across multiple business domains.

About the Role

We are seeking a proactive Senior Big Data Engineer to join our team. In this role, you will be responsible for designing, developing, and maintaining scalable data pipelines and reporting-ready data assets within Palantir Foundry. The ideal candidate will possess a robust background in cloud technologies, data architecture, and a passion for solving complex data challenges.

You will collaborate with a cross-functional team and stakeholders to translate business requirements into scalable data solutions, enabling reporting, improving data quality, and driving data transformation initiatives.

Key Responsibilities

• Collaborate with cross-functional teams to gather business requirements and design, implement, and maintain scalable data pipelines in Palantir Foundry, ensuring end-to-end data integrity and optimized workflows.
• Design and maintain data ingestion, transformation, and orchestration pipelines using Palantir Foundry, Python, and PySpark.
• Develop reporting-ready datasets and data models to support downstream analytics and Power BI reporting.
• Develop, optimize, and maintain efficient ETL/ELT processes to collect, process, and integrate data from multiple sources, ensuring timely and accurate data delivery.
• Implement data quality validation, monitoring, and health checks to ensure reliability and consistency of data assets.
• Monitor pipeline performance, identify bottlenecks, troubleshoot production issues, and continuously improve scalability and processing efficiency.
• Plan and prioritize work independently, communicate progress transparently, and proactively manage changing priorities while proposing practical solutions when requirements evolve.
• Maintain clear technical documentation and promote automation and continuous improvement across data engineering processes.
• Stay current with emerging technologies and industry best practices, incorporating innovative approaches into data engineering solutions.

Required Qualifications

• 5+ years of experience in data engineering, preferably within the pharmaceutical or life sciences industry.
• Strong proficiency in Python and PySpark.
• Proficiency with big data technologies (e.g., Apache Hadoop, Spark, Kafka, BigQuery, etc.).
• Hands-on experience with cloud services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow).
• Expertise in data modeling, data warehousing, and ETL/ELT concepts.
• Hands-on experience with database systems (e.g., PostgreSQL, MySQL, NoSQL, etc.).
• Proficiency in containerization technologies (e.g., Docker, Kubernetes).
• Effective problem-solving and analytical skills, coupled with excellent communication and collaboration abilities;

Nice to have

• Experience with market research data
• Familiarity with Veeva CRM, Reltio, SAP, and/or Palantir Foundry
• Background in pharma or healthcare
• Experience with automation / pipeline optimization

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

DevSecOps Engineer (Senior)

Location: Remote
 

About the Role

We are looking for a highly skilled and strategic Senior DevSecOps Engineer to lead our security operations and infrastructure automation initiatives. In this role, you will bridge the gap between development, security, and operations, ensuring our on-premise environment remains secure, resilient, and efficient.

You will act as a subject matter expert, leveraging cutting-edge tools like CrowdStrike to automate threat detection and response, while maintaining robust perimeter security via Cloudflare.

Key Responsibilities

• Implementation and automation of security tools in the CI/CD process (SAST, DAST, SCA).
• Analysis of vulnerabilities in code, infrastructure and containers.
• Development and configuration of security monitoring systems, incident response.
• Monitoring and improving the security level of on-prem infrastructures.
• Interaction with developers, DevOps and security teams to implement secure practices.
• Active participation in the processes of IAM configuration, management of certificates, secrets and access policies.
• Conducting internal security audits, participation in external (e.g. penetration) tests.

Requirements

Must-have:

• Experience: 5+ years of experience in DevSecOps, Security Engineering, or a related field, with at least 2 years in a Senior role.
• Tooling Expertise: Deep hands-on experience with the following specific tools:
• Baremetal/on-premise infrastructure
• Docker (Docker Swarm is a strong plus)
• Gitlab CI 
• CrowdStrike
• Cloudflare
• ELK.
• SIEM Proficiency: Proven experience deploying and tuning SIEM solutions (e.g., Splunk, Elastic Security, Datadog, or similar).
• Infrastructure: Strong background in managing on-premise infrastructure (physical servers, data centers, virtualization, networking).
• Scripting: Proficiency in Python, Go, or Bash for automation and tooling.
   

Nice-to-have:

• Experience with Kubernetes and such tools as Wiz.io and Torq.
• Previous involvement in high-load, regulated, or 24/7 production environments (e.g., iGaming, FinTech, telecom or similar).

We offer

• Fully remote work opportunity
• Enhanced leave benefits:
• 20 days of paid vacation
• 5 paid days off
• 14 paid sick leaves
• Company clubs & communities
• Celebration gifts for personal milestones
• Corporate online events, raffles & challenges
• Corporate English program
• Corporate yoga classes
• Team-building activities
• Coworking compensation
• Training and development programs (internal & external).

ZentixSoft is looking for a Senior AWS Cloud Data Engineer (Financial Ecosystems) ☁️📊

We are significantly expanding our data engineering and analytics direction for our strategic international partners and are looking for a highly mature Senior Data Engineer to drive cloud-native banking data products.
 

Note: This is an Outstaffing role where you will join a high-impact core data team. 

⚠️ A Quick Note for Applicants: To move forward swiftly, your CV must clearly highlight your hands-on architecture decisions, specific AWS technologies used, data volumes, and the precise banking data products you have delivered.

💡 Why ZentixSoft? 
💸 Salary Review: A structured system of compensation reviews aligned with your expertise and engineering growth. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

If you are ready to architect resilient data solutions for a massive financial ecosystem, here is what our perfect match looks like:

1️⃣ Engineering Seniority: A proven track record of designing, building, and operating large-scale data platforms specifically within the AWS cloud environment. 
2️⃣ FinTech & Banking Domain: Solid experience handling sensitive financial data products (focused on areas like Risk, Compliance, Fraud Prevention, Finance, or Regulatory Reporting). 
3️⃣ Governance Mastery: Deep understanding of banking infrastructure needs, including strict data lineage, quality validation, auditability, and data security. 
4️⃣ Independence: Ability to make standalone architectural and technical design choices, turning raw business requirements into robust, high-volume production pipelines. 
5️⃣ Communication: Fluent English combined with strong stakeholder management skills to align easily with business, tech, and compliance teams.
 

Project Details: 
📍 Location: Primarily remote, with a preference for candidates located in the Baltics (or other EU countries). 
✈️ Work Model: Occasional onsite visits to client offices in the Baltics (approx. once per month for 2–3 days). 
⏳ Duration: Long-term cooperation. 
🚀 Start: ASAP.

Main Responsibilities:

Data Engineer is responsible for helping select, deploy, and manage the systems and infrastructure required of a data processing pipeline to support customer requirements.

You will work on cutting-edge cloud technologies, including Microsoft Fabric, Azure Synapse Analytics, Apache Spark, Data Lake, Data Bricks, Data Factory, Cosmos DB, HD Insights, Stream Analytics, Event Grid in the implementation projects for corporate clients all over EU, CIS, United Kingdom, Middle East.

Our ideal candidate is a professional passionated with technologies, a curious and self-motivated person.

Responsibilities revolve around DevOps and include implementing ETL pipelines, monitoring/maintaining data pipeline performance, model optimization

Mandatory Requirements:

– 3+ years of experience, ideally within a Data Engineer role.

– understanding of data modeling, data warehousing concepts, and ETL processes

– 2+ years of experience with Azure Cloud technologies

– experience in distributed computing principles and familiarity with key architectures, broad experience across a set of data stores (Azure Data Lake Store, Azure Synapse Analytics, Apache Spark, Azure Data Factory)

– Understanding of landing, staging area, data cleansing, data profiling, data security and data architecture concepts (DWH, Data Lake, Delta Lake/Lakehouse, Datamart)

– SQL-skills

– communication and interpersonal skills

– English —В2

Will be beneficial if a candidate has experience in SQL migration from on-premises to cloud, data modernization and migration, advanced analytics projects, and/or professional certification in data&analytics.

We offer:

– professional growth and international certification

– free of charge technical and business trainings and the best bootcamps (worldwide, including HQ Microsoft- Redmond courses)

– innovative data & analytics projects, practical experience with cutting-edge Azure data&analytics technologies at various customers’ projects

– great compensation and individual bonus remuneration

– medical insurance

– long-term employment

– ondividual development plan

Looking for a Data Engineer to join the Dataforest team. If you are looking for 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.

Dataforest is looking for a growth-oriented Data Engineer to join our friendly team. As a Data Engineer, you will solve engaging problems using cutting-edge technologies for data collection, processing, analysis, and monitoring.

If you aren't afraid of challenges, this position is just for you!

You'll be a great fit if you have:

- 1+ years of experience as a Data Engineer;
- Strong experience with Python;
- Proven experience with Databricks and Datafactory;
- Experience with AWS/Azure;
- Experience building ETL / ELT pipelines;
- Proficiency in SQL.

What you'll do:

- Developing ETL/ELT pipelines and data management solutions;
- Implementing data processing algorithms;
- Writing and optimizing SQL queries for data extraction and analysis;
- Analyzing data and applying processing algorithms to solve business problems.

We ensure your growth with:

- Working with a high-skilled engineering team on engaging and complex projects;
- Hands-on experience with the latest technologies;
- Direct communication with international clients and challenging tasks;
- Opportunities for personal and professional growth;
- Competitive salary pegged to USD;
- Paid vacation and sick leave;
- Flexible working hours;
- Friendly working atmosphere with zero bureaucracy;
- Plenty of team traditions — corporate parties, team buildings, themed events, and much more!

Meet the YozmaTech

YozmaTech isn’t just another tech company – we’re a global team of go-getters, innovators, and A-players helping startups and product companies scale smarter and faster. We build dedicated development teams across 10+ countries, creating strong, long-term partnerships based on trust, transparency, and real impact. Here, every idea counts. We value people who are proactive, open-minded, and ready to grow. If you’re passionate about building meaningful products and want to join a team that feels like family – you’ll feel right at home with us.

Our client is a leading video monetization platform used by hundreds of digital publishers worldwide to recommend high-quality video content to their users. As part of the Platform Team, you will help build and evolve client's core management platform - a mission-critical Video CMS used by internal teams and publisher clients to manage video libraries, campaigns, placements, business rules and to track performance across the network. You will work primarily on the PHP 8 backend, Platform REST APIs, and a React/TypeScript frontend that powers the modern management UI. The platform functions as a high-scale Video CMS, handling complex workflows for fetching, processing, and managing content at scale. The system manages large-scale video and advertising operations — thousands of assets, millions of videos, and workflows that directly affect publisher operations and video delivery worldwide using modern databases and data stores like MySQL/MariaDB, Redis and OpenSearch.

Key Requirements:
🔹 5+ years of experience in backend or full-stack development;
🔹 Strong Backend Foundations - Deep understanding of client/server architecture and core backend workflows;
🔹 PHP Experience;
🔹 React & TypeScript - Strong advantage;
🔹 Data & Cloud Savvy - Hands-on experience with modern data stores (MySQL/MariaDB, Redis, OpenSearch) and cloud environments (AWS preferred);
🔹 Collaboration & Automation - Comfortable with standard engineering workflows (Git, Jira, Code Reviews) and automated testing concepts;
🔹 Soft Skills - Excellent communication skills, collaborative mindset, and;
🔹 English proficiency;
🔹 English – Upper-Intermediate or higher.

Will be plus:
🔹 Domain Experience - Background in Ad-Tech, video publishing, or media systems;
🔹 Advanced API Design - Experience with complex REST APIs (auth, permissions, pagination, and bulk operations);
🔹 Data Architecture - Strong background in algorithmic thinking and data sync/consistency across relational DBs and search indexes;

What you will do:
🔹 Contribute to Core Architecture - Design and implement scalable backend systems, REST APIs, and complex workflows while evaluating database performance and collaborating with DevOps on AWS infrastructure;
🔹 Build Features End-to-End - Deliver backend logic and React/TypeScript UI for videos, channels, templates, and placements;
🔹 Drive Technical Excellence - Ensure stability and efficiency of heavy backend processes, scheduled jobs, and data sync across core data stores;
🔹 Impact the Roadmap - Collaborate with the Team Leader and Product to translate requirements into technical designs;
🔹 Embrace Modern Tooling - Leverage AI-assisted development tools and;
🔹 Partner with QA on automated testing (transitioning to Playwright);

Interview stages:
🔹 HR Interview;
🔹 Intro-call with client;
🔹 Manager Interview (1h);
🔹 Live coding session (3h);
🔹 VP Interview (1h);
🔹 Reference check;
🔹 Offer;


Why Join Us?

At YozmaTech, we’re self-starters who grow together. Every day, we tackle real challenges for real products – and have fun doing it. We work globally, think entrepreneurially, and support each other like family. We invest in your growth and care about your voice. With us, you’ll always know what you’re working on and why it matters.
From day one, you’ll get:
🔹 Direct access to clients and meaningful products;
🔹 Flexibility to work remotely or from our offices;
🔹 A-team colleagues and a zero-bureaucracy culture;
🔹 Opportunities to grow, lead, and make your mark.

After you apply

We’ll keep it respectful, clear, and personal from start to offer.
You’ll always know what project you’re joining – and how you can grow with us.

Everyone’s welcome

Diversity makes us better. We create a space where you can thrive as you are.

Ready to build something meaningful?

Let’s talk. Your next big adventure might just start here.

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

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

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

Responsibilities

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

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

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

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

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

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

Requirements

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

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

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

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

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

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

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

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

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

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

Nice-to-have: 

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

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

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

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

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

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

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

Responsibilities

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

Requirements

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

Culture & Mindset

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

What We Offer

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

About the company: A product international fintech company that has been working with global banks and funds since 2011. It provides a low-latency gateway allowing instant transactions on global currency exchanges. The company works with microwave transmitters and other hardware solutions to reduce signal processing time. It offers its clients hosting services for automated trading equipment.

Work format: Remote.

Required experience:

• Experience deploying infrastructure from scratch and maintaining it for data projects
• Experience administering and supporting time-series databases at a production level

• Hands-on experience with at least one of the following databases: ClickHouse, InfluxDB, ScyllaDB/Cassandra, or KDB+ (experience with other time-series/NoSQL solutions will also be considered)

   

Nice to have:

• Experience making architectural decisions for building data infrastructure (technology selection, storage schema design, scaling)

   

Responsibilities:

• Selecting, installing, configuring, and scaling databases
• Designing data schemas and storage strategies for large volumes of data
• Building and tuning complex queries to calculate business metrics (Effective Spread, Last Look Hold Times)

• Training GUI developers and the team on working with data, acting as a subject matter expert

   

The company offers:

• 15 working days of vacation + public holidays off
• 10 sick days
• Medical insurance compensation
• Equipment

About the Product

We are building a US-based, data-driven product with a strong focus on scalability, performance, and cost efficiency.

Our mission is to design a modern data platform that transforms raw behavioral and monetization data into reliable, actionable business insights — in near real-time.

For us, data engineering is not just about moving data.
It’s about:

• Designing resilient architecture
• Optimizing for performance and cost
• Building reliable automation
• Ensuring architectural integrity at scale
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Role Overview

We are looking for a Senior Data Engineer who will take ownership of the data platform architecture and drive technical excellence across ingestion, modeling, and performance optimization.

This role requires deep expertise in SQL, Python, AWS infrastructure, and modern data stack principles. You will not only build pipelines — you will define standards, lead architectural decisions, and proactively improve system efficiency.

You will play a critical role in ensuring that data flows seamlessly from event streams to business-ready datasets while maintaining high performance, reliability, and cost control.

What Makes This Role Senior-Level

As a Senior Data Engineer, you will:

• Own architectural decisions for the data platform
• Identify scalability bottlenecks before they become incidents
• Optimize data infrastructure for performance and cost
• Lead technical code reviews and set engineering standards
• Mentor mid-level engineers
• Act as a technical partner to Product and Analytics stakeholders
• Balance real-time and batch processing strategies strategically

Technical Requirements

Must-Have

Expert-Level SQL

• Complex analytical queries and window functions
• Query optimization and execution plan analysis
• Identifying and eliminating performance bottlenecks
• Reducing query complexity and compute costs
• Designing partitioning and clustering strategies

Python

• Advanced data manipulation
• Building scalable ETL/ELT frameworks
• Writing production-grade data services
• Automation and monitoring scripts

AWS Core Infrastructure

• AWS Kinesis Firehose (near-real-time data streaming)
• Amazon S3 (data lake architecture and storage optimization)
• Designing reliable ingestion layers

Version Control

• Git (GitHub / GitLab)
• Branching strategies
• Leading technical code reviews
• Enforcing best practices in code quality

Nice-to-Have

Modern Data Stack

• dbt (modular SQL modeling, documentation, testing)
• Experience structuring layered data models (staging → intermediate → marts)

Data Warehousing

• Google BigQuery
• Slot management
• Cost-efficient querying
• Storage and compute optimization

Advanced Optimization Techniques

• Partitioning
• Clustering
• Bucketing
• Storage layout optimization

Integrations & Infrastructure

• Salesforce data integration
• Docker / ECS
• CI/CD for data workflows

AI / ML Exposure

• Supporting feature pipelines
• Understanding data requirements for ML systems

Key Responsibilities

Data Platform Architecture

• Design and maintain a scalable real-time and batch data platform
• Architect ingestion pipelines using AWS Kinesis and Python
• Ensure high availability and reliability of data flows

Real-Time Processing

• Enable near-real-time (seconds–minutes latency) data processing
• Build systems for operational alerting and anomaly detection
• Ensure early detection of monetization and traffic issues

Data Modeling

• Transform raw event data into business-ready datasets using dbt
• Design scalable, maintainable schemas aligned with product evolution

Performance & Cost Engineering

• Optimize SQL queries and storage structures
• Design cost-efficient partitioning strategies
• Monitor and reduce warehouse and infrastructure costs
• Balance real-time and batch processing appropriately

Engineering Excellence

• Lead and participate in code reviews
• Enforce high standards of performance, security, and maintainability
• Improve observability and monitoring across pipelines

Cross-Functional Collaboration

• Work closely with Data Analysts and Product Managers
• Translate business requirements into scalable technical solutions
• Clearly communicate trade-offs between speed, cost, and complexity

Type of Data We Process

• User behavior events (page views, clicks, searches, conversions)
• Ad & monetization events (impressions, clicks, CTR, attribution)
• System and integration logs (latency, errors, rate limits)

Why Real-Time Is Critical

• Detect broken ads or impression drops before revenue is lost
• Identify traffic anomalies or abuse early
• Enable same-day operational intervention
• Prevent negative user and advertiser experience

Near-real-time (seconds to minutes latency) is required for operational awareness.
Batch processing remains important for historical analysis and reporting — but not for incident detection.

Working Schedule

• Monday – Friday
• 16:00 – 00:00 Kyiv time
• Full alignment with US-based stakeholders

What We Value

• Strong ownership mindset
• Strategic thinking about architecture
• Focus on scalability, reliability, and cost efficiency
• Proactive problem-solving
• Clear communication with both technical and non-technical teams
• Ability to think beyond “just making it work”

Are you passionate about building scalable, reliable data systems that power real-world solutions? We are looking for a Senior Data Engineer to join our team and work remotely from Europe, Ukraine, or LATAM.

At Sigma Software, we partner with innovative companies to deliver cutting-edge technology solutions. This role offers the opportunity to design and operate a canonical data platform that integrates diverse external data sources, ensuring high quality and flexibility.

Why join us? You’ll work with modern technologies, collaborate with talented professionals across multiple regions, and have the freedom to experiment and innovate while contributing to impactful projects in the AdTech domain.

CUSTOMER

Our client is a fast-growing AdTech company focused on solving real-world challenges in digital advertising and data-driven marketing. They specialize in building highly resilient and scalable systems that encourage experimentation and innovation. Their data platform is designed to be both powerful and flexible, enabling engineers to introduce new ideas and leverage the latest technologies to optimize advertising performance and audience insights.

PROJECT

The project involves developing and maintaining a canonical data platform that integrates multiple external data sources. It supports both full data loads and incremental updates, ensures high data quality, and leverages AI-assisted tools for monitoring and issue detection.

Key Technologies: Java, Scala, SQL, GCP Dataflow, REST APIs, AI-assisted tools, event-driven architectures

RESPONSIBILITIES

• Design, build, and maintain reliable data pipelines to ingest and process data from external systems
• Implement integrations using Scala, ensuring scalability and resilience
• Handle both full data loads and incremental updates efficiently
• Collaborate with engineering and product teams to align on integration and data needs
• Develop and maintain a unified internal data model
• Ensure data quality, detect and resolve incorrect or incomplete data
• Utilize AI-assisted tools to improve data quality and operational efficiency
• Monitor and optimize data pipelines for production readiness

QUALIFICATIONS

• At least 5 years of experience as a Data Engineer or Backend/Data Engineer
• Strong production experience with Scala
• Proven experience integrating external systems via REST APIs (pagination, rate limiting, token-based authentication)
• Strong data modelling skills with structured, relational data
• Solid SQL knowledge
• Experience with GCP Dataflow
• Experience operating data pipelines in production environments
• Strong communication skills and ability to collaborate across teams
• At least an Upper Intermediate level of English

WILL BE A PLUS

• Experience with event-driven architectures or messaging systems
• Designing canonical schemas across multiple data sources
• Experience with LLM (Large Language Models)

PERSONAL PROFILE

• Proactive and detail-oriented
• Thrives in collaborative environments
• Enjoys solving complex integration challenges
• Comfortable experimenting with new technologies
• Committed to improving data systems

TBC is building next-generation AI systems at the intersection of biological computing, generative models, and large-scale AI infrastructure. As we scale our world-model and neural-optimizer efforts, we are looking for an optimization-focused Research Scientist / ML Engineer to improve the efficiency, latency, throughput, and deployability of large models.

This role is focused on making frontier models run faster, cheaper, and more reliably — especially LLMs, diffusion models, video generation models, and world-model systems. You will work across inference optimization, training efficiency, model compression, memory management, and GPU-level performance to help turn research systems into scalable, customer-ready products.
WORK WITH USA 

What You’ll Work On

• Optimize inference for LLMs, diffusion models, video models, and world-model systems
• Improve serving efficiency through techniques such as KV caching, batching, quantization, distillation, speculative decoding, and memory optimization
• Build and optimize high-throughput inference pipelines for large models running on GPU clusters
• Profile model performance across latency, throughput, memory usage, GPU utilization, and cost
• Implement custom kernels or low-level optimizations using Triton, CUDA, PyTorch, or related systems
• Improve training and fine-tuning efficiency for large generative models, including distributed training, checkpointing, parallelism, and data loading
• Work with research teams to identify bottlenecks in model architecture, inference paths, and deployment workflows
• Translate model performance improvements into clear customer-facing benchmarks and technical proof points
• Evaluate trade-offs across model quality, latency, cost, memory, and deployability

What We’re Looking For

• Strong background in machine learning systems, model optimization, or high-performance AI infrastructure
• Hands-on experience optimizing LLMs, diffusion models, video generation models, or other large generative systems
• Experience with one or more of:
  • Inference optimization
  • KV caching / attention optimization
  • Triton or CUDA kernel development
  • Quantization, pruning, distillation, or model compression
  • Distributed training / fine-tuning efficiency
  • GPU profiling and performance debugging
• Strong PyTorch experience and comfort working close to the model/runtime boundary
• Ability to reason about trade-offs between quality, latency, throughput, memory, and cost
• Comfortable working across research code, production systems, and benchmarking infrastructure
• Excited to work in an ambiguous, early-stage environment where optimization work directly shapes product feasibility

What Success Looks Like

• Large models run faster, cheaper, and more reliably across TBC’s core workloads
• Inference pipelines show measurable improvements in latency, throughput, memory use, and GPU utilization
• Training and fine-tuning workflows become more efficient, reproducible, and scalable
• Optimization work translates into clear product and customer value, not just internal benchmarks
• Research prototypes become deployable systems that can support demos, evaluations, and early partner use cases

Preferred Qualifications

• PhD, MS, or equivalent industry experience in Computer Science, Machine Learning, Systems, Robotics, or related field
• Prior work optimizing large-scale generative models in production or research settings
• Experience with modern inference/training stacks such as PyTorch, Triton, CUDA, vLLM, TensorRT, DeepSpeed, FSDP, Ray, or similar tooling
• Experience working with LLMs, diffusion models, video generation models, or world models