Jobs Data Engineer

Warsaw (hybrid)!

We’re looking for a Senior Data Engineer to join an international product company developing a complex B2B platform for enterprise customers worldwide.

You’ll build and optimize distributed data-processing systems using Java and modern data technologies, handling complex data workflows across interconnected services. This role offers end-to-end ownership, challenging performance problems, and the opportunity to influence technical decisions in a large-scale production environment.

What you’ll do:

• Build scalable data pipelines and distributed processing solutions.
• Develop Java services for data ingestion, transformation, and integration.
• Design data models and optimize database performance for large datasets.
• Improve system reliability, fault tolerance, and processing efficiency.
• Troubleshoot production issues and optimize distributed workloads.
• Use AI-assisted development tools to accelerate delivery while maintaining code quality.

What we’re looking for:

• Strong professional Java development experience.
• Deep understanding of distributed systems, concurrency, and JVM performance.
• Hands-on experience building and optimizing distributed data pipelines.
• Strong knowledge of data modeling, database design, and query optimization.
• Experience with messaging systems and integration between distributed services.
• Ability to take ownership of complex technical challenges and production systems.
• Practical experience with AI-assisted coding tools.

Nice to have: Real-time data processing, analytical databases, CI/CD, Docker, Kubernetes, TypeScript, or React.

Brainway is looking for a Senior Data Engineer to join a client project developing an AI-powered B2B platform.

You will help turn product data, customer data and external business data into reliable analytics that people can use to make decisions. Working with an existing product and analytics environment, you will develop data models, define meaningful metrics and deliver analytical features together with engineering and product teams.

This role combines hands-on data work with product thinking. You should be comfortable investigating unfamiliar datasets, asking what a metric actually means and following the work through to a useful, validated result.

What you will do

• Work with product and business stakeholders to turn questions into clear analytical requirements and measurable outcomes.
• Explore existing datasets, understand their business context and resolve inconsistencies in how data is interpreted.
•  Design analytical data models, write efficient SQL and build the transformations needed for reporting and product analytics.
•  Develop dashboards and customer-facing analytical features that make complex information easy to understand.
•  Define how new data sources should be integrated, including field mappings, transformation rules and acceptance checks.
•  Validate calculations and outputs, investigate discrepancies and improve data quality, performance and reliability.
•  Work with engineers on secure handling of customer data and appropriate access to analytical outputs.

•  Use AI-assisted tools to accelerate investigation and delivery while keeping results explainable, reproducible and checked.

   

What we are looking for

• 5+ years of relevant experience across data engineering, analytics engineering, BI or a closely related discipline.
• Strong SQL and practical experience designing data models and defining business metrics.
• Experience delivering dashboards or analytical products used in production.
• Hands-on experience with PostgreSQL, BigQuery or comparable relational and analytical platforms.
• A solid understanding of data transformation, integration and validation practices.
• The ability to work through incomplete requirements, ask useful questions and make sound product decisions.
• Clear communication with technical and non-technical stakeholders, including customers when needed.
• Confidence using AI tools alongside independent technical judgment and careful verification.
• Strong written and spoken English.
   

Useful additional experience

• Embedded analytics or customer-facing BI.
• Semantic layers, shared metric definitions, data governance or data-quality monitoring.
• Integrations with CRM, ERP, HRIS or other enterprise systems.
• APIs, Node.js or TypeScript.
• Version-controlled data and analytics workflows.

• Product analytics or analytics for enterprise operations.

   

Why this role

You will work at the intersection of engineering, analytics and product, with ownership of how business questions become dependable analytical capabilities. The work includes improving an existing foundation and helping shape how new data sources and analytical features fit into the product.
 

If you enjoy understanding the problem behind a report as much as building the data model behind it, we would like to hear from you.

We are an IT company looking for proactive, communicative engineers to join a product team on a long-term basis. We focus on results and care about the growth of our people.

Currently, we are looking for a Data Engineer. If you want to build a data platform from the ground up and see how your decisions shape a product, join us.

About the project: An AI-driven product that analyses financial documents. We are building its data platform on AWS and Snowflake.

Required skill set:

• 3+ years of experience in data engineering.
• Hands-on experience with Snowflake.
• Experience with AWS data services (S3, Glue, Lambda, etc.).
• Strong SQL and Python.
• Experience implementing ETL/ELT processes.
• Experience building and maintaining data pipelines.

Will be a plus:

• dbt, Airflow or similar orchestration tools.
• Experience processing unstructured documents.
• Understanding of GDPR and personal data handling.
• Experience with AI-assisted development tools (Claude Code, Copilot, etc.).

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 Data Engineer/Golang. 

Details: 

Engagement: Full-time contract, 6 months to start, with a strong chance of extension

Fully remote

Monday to Friday, 8 hours a day, with overlap with the team’s core working hours

Start: as soon as possible

Tech stack

Go (orchestration), PostgreSQL with pgvector, embedding models served through Amazon Bedrock.

What you’ll do

Take over the current ingestion flow and document how it works: sources, transformations, weak points and failure scenarios

Automate ingestion, validation, deduplication and incremental updates, and turn onboarding of new data sources into a repeatable process

Build safe recovery mechanisms (retries, checkpoints, replay and backfill, quarantine) so that no data is lost or duplicated

Track record counts, metadata, source identifiers and data lineage; catch schema changes and run quality checks

Support the interfaces of the matching service and take part in improving embeddings, indexing and match quality

Set up monitoring and alerting together with DevOps (freshness, failures, throughput, latency, cost)

Keep tests, deployment practices and runbooks in shape so the system can be run and recovered without you

Apply the agreed rules on data consent, access, retention and deletion, and flag any open questions early

Work closely with Product, Engineering, DevOps and ML specialists, keep the team updated on progress

What we expect

Proven ownership of production data pipelines, including debugging and recovering failures

Strong SQL and PostgreSQL data modelling skills

Practical experience with Go for orchestration and migrations

Solid understanding of APIs, batch and async processing, schema evolution, metadata and lineage

Experience with idempotent processing, reconciliation and safe reruns

Hands-on work with vector search and embedding pipelines in production (search, matching or ML products)

Ability to trace quality problems across inputs, embeddings, indexes and downstream results

Cloud experience, automated testing, code review and disciplined deployments

Monitoring, alerting, runbooks, privacy-aware data modelling and access control

Hiring process

CV review, technical interview, live coding session, final interview.

N-iX is a global software engineering and technology services company, building solutions for clients since 2002. Our teams work across Europe, the Americas, and Asia for Fortune 500 companies and fast-growing businesses in finance, retail, manufacturing, telecom, healthcare, and more.

Our Data & Analytics practice designs and delivers modern data platforms on Snowflake, Databricks, Azure and AWS, from first assessment to production at scale.

We are looking for a Data Architect to lead the design of cloud data platforms for our clients. You will own the target architecture end-to-end, work directly with client stakeholders, and guide engineering teams through delivery.
The role mixes hands-on technical leadership with consulting: you will shape solutions in pre-sales, run discovery workshops, and stay accountable for architecture quality once the project is live.

Responsibilities:

• Design end-to-end data architectures on Snowflake and/or Databricks, deployed on Azure and/or AWS: ingestion, storage, processing, serving and consumption layers.
• Define lakehouse and data warehouse patterns (medallion architecture, data vault, dimensional models) and choose the right one for each use case.
• Lead migrations from legacy platforms (on-prem DWH, Hadoop, Teradata, Oracle, SQL Server) to the cloud, including assessment, roadmap and cutover plan.
• Set standards for data modelling, pipeline design, CI/CD, infrastructure as code and environment strategy.
• Design data governance, security and compliance: access control, data lineage, cataloguing (Unity Catalog, Snowflake Horizon, Purview), GDPR and local LATAM privacy laws.
• Optimize platform cost and performance (warehouse sizing, cluster policies, FinOps practices).
• Run discovery workshops and architecture reviews with client business and technical stakeholders, in English.
• Support pre-sales: solution design, effort estimates, technical sections of proposals and client presentations.
• Mentor data engineers and review their designs and code; act as the technical point of escalation on the project.
• Prepare data foundations for BI, advanced analytics, ML and GenAI use cases.

Requirements: 

• 8+ years in data engineering or data platforms, including 3+ years as a Data or Solution Architect.
• Hands-on production experience with Snowflake and/or Databricks (at least one in depth).
• Strong knowledge of Azure and/or AWS data services, for example:
  • Azure: ADLS Gen2, Data Factory, Synapse/Fabric, Event Hubs, Purview, Key Vault
  • AWS: S3, Glue, Redshift, Kinesis, Lambda, Lake Formation, IAM
• Expert SQL and solid Python or Scala; experience with Spark.
• Proven experience with data modelling (dimensional, data vault) and lakehouse design.
• Experience with orchestration and transformation tools such as Airflow, dbt, Azure Data Factory or Databricks Workflows.
• Working knowledge of infrastructure as code (Terraform, Bicep or CloudFormation) and CI/CD for data.
• Track record of leading at least one cloud data platform build or migration from design to production.
• Client-facing consulting experience: running workshops, presenting architecture decisions, managing expectations.
• At least B2 English, written and spoken.

Nice to have:

• Certifications: SnowPro Advanced Architect, Databricks Certified Data Engineer Professional, Azure Solutions Architect Expert, AWS Solutions Architect Professional or AWS Data Analytics/Data Engineer.
• Streaming architectures (Kafka, Spark Structured Streaming, Snowpipe Streaming, Delta Live Tables).
• Data mesh, data products and data contracts in practice.
• MLOps and GenAI data foundations (feature stores, vector search, RAG pipelines).
• Domain experience in banking, insurance, retail, telecom or energy.

Our customer is a leader in global prestige retail, teaching and inspiring clients to play in a world of beauty. The company has earned its reputation all over the world as a beauty trailblazer with its expertise, innovation, and entrepreneurial spirit. Our project is about developing a full-stack e-Commerce application for the world's biggest cosmetics retailer chain of stores.

PROJECT TECHNOLOGIES AND TOOLS

SQL, HIVE

Hadoop

Kafka

Spark

Spark Streaming

Cloud (Azure)

Scala or Python

RESPONSIBILITIES

Investigation and requirements clarification

Solution architecture design from scratch

PoC development

Full project delivery

Interactions with the customer development team and 3-rd party vendor

REQUIREMENTS

5+ years of experience in Java/Scala or Python

Experience with HIVE

Strong communication skills

Intermediate or higher English level, both spoken and written (B1+)

NICE TO HAVE

Databricks

Avro

Spark

Apache Hadoop

WE OFFER

Competitive compensation depending on experience and skills

Individual career path in engineering

Social package - medical insurance, sports

Compensation for sick lists and regular vacations

English classes with certified English teachers

Flexible work hours

Project Description:

Surveillance Enhancement Program (SEP) is a multi-year program (MYP) to build and enhance the surveillance platform to detect potential instances of market misconduct, market manipulation and market abuse. The Bank met its commitment to FCA (Financial Conduct Authority, FCA) to meet Market Abuse Regulation ("MAR") requirements for critical data sources in September 2017. Following that, subsequent projects were initiated to further enhance, expand and complement the current coverage of the automated surveillance platform. This project will focus on Alerts enhancements, Strategic data sourcing from FM order and trade lakes, UI / workflow enhancements to meet regulatory obligations (i.e. Market Abuse Regulation, Dodd Frank). This also includes Control Room & Suitability Monitoring related data, UI and scenario enhancements.

Responsibilities:

• Develop software applications based on business requirements
• Maintain software applications and make enhancements according to project specifications
• Participate in requirement analysis, design, development, testing, and implementation activities
• Propose new techniques and technologies for software development.
• Perform unit testing and user acceptance testing to evaluate application functionality
• Ensure to complete the assigned development tasks within the deadlines
• Work in compliance with coding standards and best practices
• Provide assistance to Junior Developers when needed.
• Perform code reviews and recommend improvements.
• Review business requirements and recommend changes to develop reliable applications.
• Develop coding documentation and other technical specifications for assigned projects.
• Act as primary contact for development queries and concerns.
• Analyze and resolve development issues accurately.

Mandatory Skills Description:

• 15+ years of experience in data intensive Pyspark development ,Window Functions , Performance Optimization ,Spark Architecture,Salting ,Column Pruning
• Must have experience in leading a Development Team
• Proven experience as a Core Python developer
• Good understanding of Gen AI
• Experience developing Classes, OOPS, exception handling, parallel processing
• Strong knowledge of DB connectivity, data loading , transformation, calculation
• Extensive experience in Pandas/Numpy dataframes, slicing, data wrangling, aggregations
• Lambda Functions, Decorators
• Vector operations on Pandas dataframes /series
• Application of apply map, apply, map functions
• Concurrency and error handling data pipeline batch of size [1-10 gb]
• Ability to understand business requirements and translate them into technical requirements
• Ability to design architecture of data pipeline for concurrent data processing
• Working with Git source control Service-orientated architecture, including the ability to consider integrations with other applications and services
• Debugging application

Nice-to-Have Skills Description:

• Knowledge of web backend technology - Django, Python, PostgreSQL.
• Apache Airflow
• Atlassian Jira
• Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities & Credit), various trade types (OTC, exchange traded, Spot, Forward, Swap, Options) and related systems is a plus
• Surveillance domain knowledge, regulations (MAR, MIFID, CAT, Dodd Frank) and related Systems knowledge is certainly a plus

GR8_TECH builds B2B iGaming platforms for operators who play to lead.

We deliver full-cycle, high-impact tech designed to scale — from seamless integrations and expert consulting to long-term operational support. Our platform powers millions of active players and drives real business growth. Call it what it is: the iGaming Platform for Champions.

With 1000+ GR8 people across locations and time zones, we don’t just ship technology — we help operators build success stories across brands, markets, and geos.

Our ambition drives us. Our people make it real.

If you’re a challenger in spirit and a champion in action — join us.

Why this role exists:

This role exists to scale and stabilize the core data infrastructure powering our real-time analytics and decision systems. You’ll help us deliver accurate, zero-latency business insights by designing robust ingestion pipelines, building clean semantic models, and maintaining rock-solid data governance across the platform.
 

What you’ll drive:

Architecture & Data Modeling

• Design and implement scalable data transformation layers, organizing raw data into validated, production-grade data marts.
• Build and optimize semantic layer models, ensuring consistent metrics and definitions across all reporting surfaces.
• Establish data modeling standards utilizing optimal storage formats (Parquet, ORC, AVRO) and storage engines (PostgreSQL, ClickHouse, Redshift/BigQuery).

Pipeline Delivery & Execution

• Ship and maintain end-to-end data ingestion pipelines integrating internal services and third-party event streams (Kafka, Kinesis, external APIs).
• Automate data quality and integrity testing to catch pipeline breaks and silent schema drifts before they reach downstream consumers.
• Tune query performance and pipeline bottlenecks, actively managing indexing, data partitioning, and job scheduling.

Platform Operations & Integration

• Expose data-centric services by building and managing performant REST APIs using modern frameworks (FastAPI, Flask, or Aiohttp).
• Enforce granular access control across our data layers via RBAC, row-level security (RLS), and column-level masking.
• Automate CI/CD workflows and deployments using Docker, GitLab CI, and Terraform across our cloud infrastructure.

Engineering Quality & Cross-Team Alignment

• Partner with product managers, software engineers, and analysts to translate business logic into maintainable technical requirements.
• Participate in thorough code and architecture reviews, upholding strict testing standards (unit, integration, TDD/BDD).
• Document system designs and data catalog schemas, making data discoverable and self-serve across the organization.

What makes you a GR8 fit:

Must-have

• 3+ years of production experience in Data Engineering, Software Engineering, or related distributed data systems.
• Proven SQL & Database expertise: Strong hands-on work with relational databases (PostgreSQL) and modern analytical/columnar data warehouses (ClickHouse, BigQuery, or Redshift).
• Demonstrated pipeline & distributed compute skills: Practical experience orchestrating distributed jobs (Spark, Trino, Flink) and handling event streaming engines (Kafka, RabbitMQ, or Kinesis).
• Solid software engineering foundations: Proficient in writing clean, modular Python for data applications, pipeline orchestration, and REST APIs.
• Linux, Container, & CI/CD literacy: Comfortable writing Dockerfiles, configuring CI/CD pipelines (GitLab CI, GitHub Actions), and utilizing Git version control.
• Data quality discipline: Direct experience implementing automated data testing, schema validation, and pipeline observability.

Nice-to-have

• Hands-on experience with Infrastructure as Code (Terraform) and AWS cloud services.
• Practical exposure to metadata management tools (DataHub, OpenMetadata).
• Understanding of resilience patterns in distributed systems (circuit breakers, rate limiting, connection pooling).
• Degree in Computer Science, Software Engineering, or equivalent practical experience.

Why you’ll love working here:

Benefits Cafeteria — annual budget you allocate to:

• Sports • Medical • Mental health • Home office • Languages.

Work-life & support

• Paid maternity/paternity leave + monthly childcare allowance.
• 20+ vacation days, unlimited sick leave, emergency time off.
• Remote-first + tech support + coworking compensation.
• Team events (online/offline/offsite).
• Learning culture with internal courses + growth programs.

Our culture & core values:

GR8_TECH culture is how we win — through trust, ownership, and a growth mindset. We move fast, stay curious, and keep it real, with open feedback, room to experiment, and a team that’s got your back.

FUELLED BY TRUST: we’re open, honest, and have each other’s backs.

OWN YOUR GAME: we take initiative and own what we do.

ACCELER8: we move fast, focus smart, and keep it simple.

CHALLENGE ACCEPTED: we grow through challenges and stay curious.

BULLETPROOF: we’re resilient, ready, and always have a plan.

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to strengthen our data foundation through good modeling, clean schemas, reliability and automation.

Responsibilities

Work closely with product teams to help define and execute technical criteria

Architect and maintain robust data ingestion pipelines that feed both our application and analytics

Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data

Help onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with our existing services and workflows

Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema change handling

Build a documented, well-tested data foundation that serves the app, analytics and future AI use cases

Skills

Must have

Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous IC

Designing and building production ingestion pipelines from APIs, files and shared-data sources, using a workflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)

Production experience with a cloud data warehouse (Snowflake preferred)

Strong SQL, plus production experience in Python (preferred) or Node.js/TypeScript

AWS or another enterprise cloud platform

AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD

Strong English and communication skills

Nice to have

dbt

Preparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)

Infrastructure as code (e.g. Terraform)

Exposure to energy, utilities or sustainability data

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

Обовʼязки:

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

Вимоги:

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

Буде плюсом:

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

Project Description:

Join our Development Center in Bucharest, and become a member of our open-minded, progressive and professional team. In this role you will be working on projects for one our world famous clients.
You will have a chance to grow your technical and soft skills, and build a thorough expertise of the industry of our client.
On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.

Responsibilities:

We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud. This role focuses on building reliable, cloud native data products that enable advanced reporting, analytics, and decision making across the organization.

Key Responsabilities:

• Build scalable data pipelines: Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting.
• Develop SQL and BigQuery solutions: Write and optimize advanced SQL transformations and build performant, cost‑efficient BigQuery data models.
• Develop Python workflows: Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high‑quality code.
• Design data models and ensure quality: Build robust data models and apply validation practices to maintain accuracy and reliability.
• Build cloud‑native data solutions: Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms.
• Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance.
• Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards.

Mandatory Skills Description:

• 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems.
• Strong expertise in SQL and hands‑on experience building performant datasets in BigQuery (or similar cloud data warehouses).
• Proven experience with Python and PySpark for scalable data processing in distributed environments.
• Solid understanding of data modeling, ELT/ETL patterns, and data quality best practices.
• Experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services.
• Hands‑on experience building scalable data pipelines (batch and near real‑time) in a cloud‑native environment.
• Proficiency with version control, CI/CD pipelines, and automated testing frameworks.
• Ability to troubleshoot and optimize performance across compute, storage, and processing layers.

Nice-to-Have Skills Description:

• Experience with Infrastructure as Code (Terraform, Ansible, Chef).
• Knowledge of shell scripting.
• Experience in financial services or regulated environments

Languages:

English: B2 Upper Intermediate

Job Description

Candidate evidence
Candidates should be prepared to present a sanitised example of work they personally led or substantially
contributed to, such as an enterprise conceptual model, business or domain ontology, taxonomy or cross-system
semantic harmonisation initiative. Experience limited to physical schemas, dimensional models or ERDs derived
from source systems is not sufficient.

Must-have experience

• Demonstrable hands-on delivery of conceptual data models in complex enterprise environments.
• Practical experience developing or applying business or domain ontologies, taxonomies or related knowledge-representation models.
• Experience defining concepts independently of application schemas and vendor technologies.
• Experience reconciling conflicting terminology and entity representations across systems or business
  domains.
• Strong understanding of identity, classification, relationships, cardinality, grain, time, lifecycle and business
  rules.
• Proven ability to facilitate decisions with business experts and architecture stakeholders and communicate clearly in English.
• Ability to translate conceptual work into logical models and practical implementation guidance.

Nice-to-have experience

• Energy, utilities, industrial infrastructure, asset management or service domains.
• Modelling assets, equipment, telemetry, maintenance, documents, media evidence or AI-generated
  observations.
• Metadata management, business glossaries, lineage, provenance and data governance.
• Knowledge graphs or standards such as RDF, RDFS, OWL, SKOS or SHACL.
• Dimensional modelling, Data Vault, modern lakehouse patterns and open semantic specifications such as
  Apache Ossie.
• Awareness of Microsoft Azure, Fabric, Databricks, dbt and multi-region or multi-tenant data platforms.

Job Responsibilities

Key responsibilities
 

• Work with business experts and architects to define important concepts, terminology, classifications and
  relationships.
• Develop and maintain enterprise and domain conceptual data models, taxonomies and pragmatic business
  ontologies.
•  Distinguish business concepts from source-system records, application objects, tables and APIs.
   Reconcile conflicting terminology, identifiers and representations across SAP, Salesforce, IoT and other
  systems.
• Define entity identity, lifecycle, temporal meaning and relationships, including evidence, observations and
  machine-derived assertions.
• Translate conceptual models into logical models, data contracts and implementation guidance together with
  data architects and engineers.
• Maintain traceability from business definitions to source data and downstream data products.
• Facilitate modelling workshops, document semantic decisions and establish practical modelling standards.

Expected deliverables

•  Enterprise and domain conceptual data models
• Governed vocabulary, definitions and synonyms
• Taxonomies, classification models and a pragmatic business or domain ontology
• Entity identity, relationship, lifecycle and temporal definitions
• Concept-to-source and concept-to-logical-model mappings
• Modelling standards, semantic decision records and implementation guidance

Department/Project Description

Project context
The project  is a long-term data platform initiative for the energy infrastructure domain. It will
integrate enterprise data from SAP, Salesforce and legacy systems with IoT telemetry, documents, images and
machine-derived observations. The platform will support analytics, applications and governed AI use cases
across multiple regions, tenants and cloud environments.

Role purpose
Lead the development of the enterprise conceptual data model and supporting ontology. Establish consistent
business meaning across domains and source systems, then guide its translation into logical models, data
products and semantic definitions.

Join our team to work on enhancing a robust data pipeline that powers our SaaS product, ensuring seamless contextualization, validation, and ingestion of customer data. Collaborate with product teams to unlock new user experiences by leveraging data insights. Engage with domain experts to analyze real-world engineering data and build data quality solutions that inspire customer confidence. Additionally, identify opportunities to develop self-service tools that streamline data onboarding and make it more accessible for our users.

Our Client was established with the mission to fundamentally transform the execution of capital projects and operations. Designed by industry experts for industry experts, our Client’s platform empowers users to digitally search, visualize, navigate, and collaborate on assets. Drawing on 30 years of software expertise and 180 years of industrial legacy as part of the renowned Scandinavian business group, Client plays an active role in advancing the global energy transition. The company operates from Norway, the UK, and the U.S.

 

Key Responsibilities:

• Design, build, and maintain data pipelines using Python/PySpark.
• Collaborate with an international team to develop scalable data solutions.
• Conduct in-depth analysis and debugging of system bugs (Tier 2).
• Develop and maintain smart documentation for process consistency, including the creation and refinement of checklists and workflows.
• Set up and configure new tenants, collaborating closely with team members to ensure smooth onboarding.
• Write unit/integration tests to ensure the quality and reliability of data services.
• Work with Gitlab to manage code, CI/CD and collaborate with team members.
• Utilize Databricks for data processing and management.
• Work in Azure cloud.

Requirements:

• Programming: Minimum of 1-2 years as data/software engineer, or in a relevant field .
• Python working knowledge: Coding experience in Python, particularly in delivering/maintaining data pipelines and troubleshooting code-based bugs. Experience working with large codebases, working with an IDE and Git.
• Data Skills: Structured approach to data insights and diagnostic skills for data related issues.o data insights.
• Cloud: Familiarity with cloud platforms (preferably Azure).
• Data Platforms: Knowledge of Databricks, Snowflake, or similar data platforms.
• Database Skills: Knowledge of relational databases, and working experience with SQL.
• Documentation: Experience in creating and maintaining structured documentation.
• Testing: Proficiency in utilizing testing frameworks (pytest) to ensure code reliability and maintainability.
• Version Control: Experience with Git and Gitlab or equivalent.
• English and Polish Proficiency: B2 plus.
• Interpersonal Skills: Strong collaboration abilities, willing to learn new skills and tools, adaptive and exploring mindset. We're looking for candidates that do not fear to reach out to others.

Nice to have:

• Big Data: Experience using Apache Spark/PySpark.
• Knowledge of Docker and Kubernetes.
• Ability to travel abroad once/twice a year for an on-site team building (Oslo, Norway) .

At ZentixSoft, we are expanding our banking outstaffing operations and are looking for a strong Senior Data Engineer (Databricks) for a long-term enterprise banking initiative.
 

Below are the core details and parameters of the request:

• Domain: Banking / Financial Services.
• Format: 100% Remote.
• Duration: Initial 3-month contract with planned extension through 2029 (Long-term project).
• Start Date: ASAP.
   

🎯 Key Requirements (Must-Have):

1. Banking & ETL/ELT Experience: 5+ years of commercial experience as an ETL/ELT Data Engineer in complex financial services or banking environments handling big data sets.
2. Databricks & Core Tech: Direct production experience with Databricks (mandatory), alongside ETL/ELT tools (ODI, dbt Cloud), orchestration (Airflow, ODI), and GitHub.
3. Data Engineering & Quality: Solid background in metadata-driven development, designing scalable technical data models, performance tuning/troubleshooting, and implementing Data Quality (DQ) rules.
4. Ownership & Independence: Ability to take full ownership of tasks, perform 2nd/3rd level support, mentor team members, and drive delivery without micromanagement.
5. Communication: Strong English communication skills for effective collaboration in an Agile framework.
   
   Nice to Have
   - Experience working within the financial services industry
   - Familiarity with cloud platforms (e.g., AWS)
   - Experience with DevOps practices and tools for continuous integration and continuous deployment (CI/CD)
   - Experience with near real time processing
   - Experience with reporting tools and their inpact on data layers
   - Conceptual understanding with Data Governance tools: Glossary, Data quality, RDM
   - Prior experience with building proactive monitoring solution components for reporting infrastructure