Jobs Data Engineer

We are seeking a skilled Data Engineer to join our team and contribute to the development of large- scale analytics platforms. The ideal candidate will have strong experience in cloud ecosystems such as Azure and AWS, as well as expertise in AI and machine learning applications. Knowledge of the healthcare industry and life sciences is a plus.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines for large-scale analytics platforms.
• Implement cloud-based solutions using Azure and AWS, ensuring reliability and performance.
• Work closely with data scientists and AI/ML teams to optimize data workflows.
• Ensure data quality, governance, and security across platforms.
• Collaborate with cross-functional teams to integrate data solutions into business processes.

Required Qualifications

• Bachelor's degree (or higher) in Computer Science, Engineering, or a related field.
• 3+ years of experience in data engineering, big data processing, and cloud-based architecture.
• Strong proficiency in cloud services (Azure, AWS) and distributed computing frameworks.
• Mandatory hands-on experience with Databricks (UC, DLTs, Delta Sharing, etc.)
• Expertise in SQL and database management systems (SQL Server, MySQL, etc.).
• Experience with data modeling, ETL processes, and data warehousing solutions.
• Knowledge of AI and machine learning concepts and their data requirements.
• Proficiency in Python, Scala, or similar programming languages.
• Basic knowledge of C# and/or Java programming.
• Familiarity with DevOps, CI/CD pipelines.
• High-level proficiency in English (written and spoken).

Preferred Qualifications

• Experience in the healthcare or life sciences industry.
• Understanding of regulatory compliance related to healthcare data (HIPAA, GDPR, etc.).
• Familiarity with interoperability standards such as HL7, FHIR, and EDI.

We are looking for a specialist to design and implement an end-to-end performance testing framework for a healthcare system running on Databricks and Microsoft Azure. You will build a repeatable, automated approach to measure and improve performance across data ingestion, ETL/ELT pipelines, Spark workloads, serving layers, APIs, security/identity flows, integration components, and presentation/UI, while meeting healthcare-grade security and compliance expectations.

This role sits at the intersection of performance engineering, cloud architecture, and test automation, with strong attention to regulated-domain requirements (privacy, auditability, access controls).

Key Responsibilities

• Design and build a performance testing strategy and framework for a Databricks + Azure healthcare platform.
• Define performance KPIs/SLOs (e.g., pipeline latency, throughput, job duration, cluster utilization, cost per run, data freshness).
• Create workload models that reflect production usage (batch, streaming, peak loads, concurrency, backfills).
• Create a test taxonomy: smoke perf, baseline benchmarks, load, stress, soak/endurance, spike tests, and capacity planning.
  
   
• Implement automated performance test suites for:
  • Databricks jobs/workflows (Workflows, Jobs API)
  • Spark/Delta Lake operations (reads/writes, mergers, compaction, Z-Ordering where relevant)
  • Data ingestion (ADF, Event Hubs, ADLS Gen2, Autoloader, etc. as applicable)
• Build test data generation and data anonymization/synthetic data approaches suitable for healthcare contexts.
• Instrument, collect, and analyze metrics from:
  • Spark UI / event logs
  • Databricks metrics and system tables
  • Azure Monitor / Log Analytics
  • Application logs and telemetry (if applicable)
• Produce actionable performance reports and dashboards (trend, regression detection, run-to-run comparability).
• Create performance tests for key user journeys (page load, search, dashboards) using appropriate tooling.
• Measure client-side and network timings and correlate them with API/backend performance.
• Integrate performance tests into CI/CD (Azure DevOps or GitHub Actions), including gating rules and baselines.
• Document framework usage, standards, and provide enablement to engineering teams.

Required Qualifications

• Proven experience building performance testing frameworks (not just executing tests), ideally for data platforms.
• Strong hands-on expertise with Databricks and Apache Spark performance tuning and troubleshooting.
• Strong knowledge of Azure services used in data platforms (commonly ADLS Gen2, ADF, Key Vault, Azure Monitor/Log Analytics; others as relevant).
• Strong programming/scripting ability in Python and/or Java/TypeScript.
• Familiarity with load/performance tools and approaches (e.g., custom harnesses, Locust/JMeter/k6 where appropriate, or Spark-specific benchmarking).
• Ability to design repeatable benchmarking (baseline creation, environment parity, noise reduction, statistical comparison).
• Understanding of data security and compliance needs typical for healthcare (e.g., HIPAA-like controls, access management, auditability; adapt to your jurisdiction).
• High-level proficiency in English

Nice-to-Have / Preferred

• Experience with Delta Lake optimization (OPTIMIZE, ZORDER, liquid clustering where applicable), streaming performance, and structured streaming.
• Experience with Terraform/IaC for reproducible test environments.
• Knowledge of Unity Catalog, data governance, and fine-grained access controls.
• Experience with OpenTelemetry tracing and correlation across UI → API → data workloads.
• FinOps mindset: performance improvements tied to cost efficiency on Databricks/Azure.
• Prior work on regulated domains (healthcare, pharma, insurance).

 Working Model

• Contract
• Remote
• Collaboration with Data Engineering, Platform Engineering, Security/Compliance, and Product teams.

• Project Description:

  We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions.

   

• Responsibilities:

  - Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.
  - Develop, optimize, and maintain PySpark applications within the Databricks environment.
  - Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.
  - Write and maintain unit tests to ensure high code quality and reliability.
  - Perform data validation and quality checks to ensure compliance with defined business rules
  - Support and manage production deployments, ensuring system stability and performance.
  - Troubleshoot and resolve issues across development, testing, and production environments.
  - Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.
  - Maintain technical documentation for code, workflows, and deployment processes.
  - Pull from and push code to GitHub repositories, following version control and branching best practices.

   

• Mandatory Skills Description:

  - 5+ years of experience in data engineering roles
  - Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
  - 4+ years of experience with Databricks data pipelines
  - Strong hands-on experience with PySpark and/or Apache Spark.
  - Experience working with CI/CD pipelines and DevOps best practices.
  - Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).
  - Experience writing unit tests and performing data validation
  - Experience supporting production systems and deployments
  - Strong analytical and troubleshooting skills.

Client

Our client is one of the world’s top 20 investment companies headquartered in Great Britain, with branch offices in the US, Asia, and Europe.

Project overview

The company’s IT environment is constantly growing, with around 30 programs and more than 60 active projects. They are building a data marketplace that aggregates and analyzes data from multiple sources such as stock exchanges, news feeds, brokers, and internal quantitative systems.

As the company moves to a new data source, the main goal of this project is to create a golden source of data for all downstream systems and applications. The team is performing classic ELT/ETL: transforming raw data from multiple sources (third-party and internal) and creating a single interface for delivering data to downstream applications.

Position overview

We are looking for a Data Engineer with strong expertise in DBT, Snowflake, and modern data engineering practices. In this role, you will design and implement scalable data models, build robust ETL/ELT pipelines, and ensure high-quality data delivery for critical investment management applications.

Responsibilities

• Design, build, and deploy DBT Cloud models.
• Design, build, and deploy Airflow jobs (Astronomer).
• Identify and test for bugs and bottlenecks in the ELT/ETL solution.

Requirements

• 5+ years of experience in software engineering (GIT, CI/CD, Shell scripting).
• 3+ years of experience building scalable and robust Data Platforms (SQL, DWH, Distributed Data Processing).
• 2+ years of experience developing in DBT Core/Cloud.
• 2+ years of experience with Snowflake.
• 2+ years of experience with Airflow.
• 2+ years of experience with Python.
• Good spoken English.

Nice to have

• Proficiency in message queues (Kafka).
• Experience with cloud services (Azure).
• CI/CD knowledge (Jenkins, Groovy scripting).

Project description

We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions.

Responsibilities

Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.

Develop, optimize, and maintain PySpark applications within the Databricks environment.

Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.

Write and maintain unit tests to ensure high code quality and reliability.

Perform data validation and quality checks to ensure compliance with defined business rules

Support and manage production deployments, ensuring system stability and performance.

Troubleshoot and resolve issues across development, testing, and production environments.

Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.

Maintain technical documentation for code, workflows, and deployment processes.

Pull from and push code to GitHub repositories, following version control and branching best practices.

Skills

Must have

5+ years of experience in data engineering roles

Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).

4+ years of experience with Databricks data pipelines

Strong hands-on experience with PySpark and/or Apache Spark.

Experience working with CI/CD pipelines and DevOps best practices.

Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).

Experience writing unit tests and performing data validation

Experience supporting production systems and deployments

Strong analytical and troubleshooting skills.

Nice to have

Knowledge of FHIR (Fast Healthcare Interoperability Resources) standards.

Experience with healthcare data integration projects.

Familiarity with REST APIs and FHIR server integrations.

Experience in Agile/Scrum development environments.

Languages

English: C1 Advanced

We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions.

• Responsibilities:

  - Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.
  - Develop, optimize, and maintain PySpark applications within the Databricks environment.
  - Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.
  - Write and maintain unit tests to ensure high code quality and reliability.
  - Perform data validation and quality checks to ensure compliance with defined business rules
  - Support and manage production deployments, ensuring system stability and performance.
  - Troubleshoot and resolve issues across development, testing, and production environments.
  - Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.
  - Maintain technical documentation for code, workflows, and deployment processes.
  - Pull from and push code to GitHub repositories, following version control and branching best practices.

• Mandatory Skills Description:

  - 5+ years of experience in data engineering roles
  - Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
  - 4+ years of experience with Databricks data pipelines
  - Strong hands-on experience with PySpark and/or Apache Spark.
  - Experience working with CI/CD pipelines and DevOps best practices.
  - Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).
  - Experience writing unit tests and performing data validation
  - Experience supporting production systems and deployments
  - Strong analytical and troubleshooting skills.

• Nice-to-Have Skills Description:

  - Knowledge of FHIR (Fast Healthcare Interoperability Resources) standards.
  - Experience with healthcare data integration projects.
  - Familiarity with REST APIs and FHIR server integrations.
  - Experience in Agile/Scrum development environments.

Project Description:

We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions.

Responsibilities:

- Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.

- Develop, optimize, and maintain PySpark applications within the Databricks environment.

- Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.

- Write and maintain unit tests to ensure high code quality and reliability.

- Perform data validation and quality checks to ensure compliance with defined business rules

- Support and manage production deployments, ensuring system stability and performance.

- Troubleshoot and resolve issues across development, testing, and production environments.

- Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.

- Maintain technical documentation for code, workflows, and deployment processes.

- Pull from and push code to GitHub repositories, following version control and branching best practices.

Mandatory Skills Description:

- 5+ years of experience in data engineering roles

- Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).

- 4+ years of experience with Databricks data pipelines

- Strong hands-on experience with PySpark and/or Apache Spark.

- Experience working with CI/CD pipelines and DevOps best practice.

 Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).

- Experience writing unit tests and performing data validation

- Experience supporting production systems and deployments

- Strong analytical and troubleshooting skills.

Nice-to-Have Skills Description:

- Knowledge of FHIR (Fast Healthcare Interoperability Resources) standards.

- Experience with healthcare data integration projects.

- Familiarity with REST APIs and FHIR server integrations.

- Experience in Agile/Scrum development environments.

We are looking for an experienced Data Engineer to join a long-term B2C project. The main focus is on building Zero ETL pipelines, as well as maintaining and improving existing ones.

Responsibilities:
- Build and maintain scalable Zero ETL pipelines.
- Design and optimize data warehouses and data lakes on AWS (Glue, Firehose, Lambda, SageMaker).
- Work with structured and unstructured data, ensuring quality and accuracy.
- Optimize query performance and data processing workflows (Spark, SQL, Python).
- Collaborate with engineers, analysts, and business stakeholders to deliver data-driven solutions.

Requirements:
- 5+ years of experience in Data Engineering.
- Advanced proficiency in Spark, Python, SQL.
- Expertise with AWS Glue, Firehose, Lambda, SageMaker.
- Experience with ETL tools (dbt, Airflow etc.).
- Background in B2C companies is preferred.
- JavaScript and Data Science knowledge are a plus.
- Degree in Computer Science (preferred, not mandatory).

We offer:
- remote time job, B2B contract
- 12 sick leaves and 18 paid vacation business days per year
- Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
- Smart environment
- Interesting projects from renowned clients
- Flexible work schedule
- Competitive salary according to the qualifications
- Guaranteed full workload during the term of the contract
 

We're a small, fast-moving team building a B2B SaaS product in the e-commerce analytics space. We're looking for a skilled Data & Infrastructure Engineer to help us scale our data pipelines, optimize our analytical engine, and maintain a rock-solid production environment.

About the role

You'll work closely with our technical team to ensure our data flows seamlessly from ingestion to insight. This isn't a role where you'll be handed a perfectly architected blueprint. We need someone who can take ownership of our infrastructure, make smart trade-offs between performance and cost, and communicate clearly when architecting complex systems.

Our stack is built to handle high-volume analytics at scale. We rely on ClickHouse for our heavy lifting, Kafka for real-time data streaming, and Kubernetes for orchestration. We use dbt to keep our transformations sane and manageable.

What you'll be doing

• Scaling the Backbone: Managing and optimizing our ClickHouse clusters and Kafka pipelines to handle growing data volumes.
• Infrastructure as Code: Maintaining and evolving our Kubernetes environment to ensure high availability and performance.
• Data Modeling: Building and maintaining robust data models using dbt to support our analytics product.
• Performance Tuning: Identifying bottlenecks in data ingestion and query performance, and implementing long-term fixes.
• Reliability: Participating in technical discussions, code reviews, and debugging production infrastructure issues when they arise.

What we're looking for

• 3+ years of professional experience in Data Engineering, DevOps, or Site Reliability Engineering.
• Expertise in ClickHouse: You know how to optimize MergeTree engines and write efficient analytical queries.
• Streaming Mastery: Hands-on experience deploying and managing Kafka clusters.
• Orchestration Skills: Strong experience working with Kubernetes (K8s) in a production environment.
• SQL & dbt: A deep understanding of SQL and experience using dbt for data transformation workflows.
• Independence: Ability to manage your own time and take infrastructure projects from concept to completion.

Bonus points

• Hetzner Cloud: Experience managing bare metal or cloud instances specifically within the Hetzner ecosystem.
• Infrastructure as Code: Experience with Terraform, Pulumi, or Ansible.
• Industry Context: Background in e-commerce, Shopify, or marketing analytics.
• Backend Knowledge: Familiarity with Node.js/TypeScript to help bridge the gap between infra and the app layer.

The Process

The interview process consists of a 30-minute technical interview and, in the second round, a 1-hour coding/system design task focused on data architecture.

Candidates based in Poland are especially welcome. Proficiency in Polish will be a strong advantage. 
 

Scope of Work (Snowflake Data Engineer / Architect)

- Migration of data processing pipelines and datasets between two Snowflake instances. 

- Reverse engineering of selected existing solutions in order to assess the required level of modification (including implementation of selected adjustments). 

- Migration of several Streamlit applications (native Streamlit in Snowflake) alongside the data and pipelines. 

- For selected datasets and pipelines, implementation of new components (code, data structures, and Snowflake cross-account data sharing mechanisms). 

- Re-creation of an existing data processing framework currently used by another business unit on the new Snowflake account. 

- Ensuring that all data structures required for critical applications and Power BI reports are available in the new Snowflake account, including the pipelines/processes feeding those structures. 

-The role will require close collaboration with a Power BI specialist responsible for analyzing and prioritizing semantic models and Power BI reports currently consuming data from the existing Snowflake account.

Primary Skills: 

- SQL

 -Snowflake (preferred) or other cloud data warehouse solutions 

- Data layering 

- Stored procedure / Cursor / Dynamic SQL / Window functions / Recursive Query etc C# development 

- ETL - Azure Data Factory (or other ETL Tools)

- Data pipeline design 

- REST API / Oauth 

- Semi-structured Data 

- File Processing: JSON / XLSX / CSV / PARQUET

We offer:

• Attractive financial package

• Paid vacation, holidays and sick leaves

• Challenging projects

• Professional & career growth

• Great atmosphere in a friendly small team

• Flexible working hours

Description

As the Data Architect, you will be the senior technical visionary for the Data Platform. You will be responsible for the high-level design of the entire solution, ensuring it is scalable, secure, and aligned with the company’s long-term strategic goals. Your decisions will form the technical foundation upon which the entire platform is built, from initial batch processing to future real-time streaming capabilities.

Requirements

– Cloud Architecture: Extensive experience designing and implementing large-scale data platforms on Microsoft Azure.
– Expert Technical Knowledge: Deep, expert-level understanding of the Azure data stack, including ADF, Databricks, ADLS, Synapse, and Purview.
– Data Concepts: Mastery of data warehousing, data modeling (star schemas), data lakes, and both batch and streaming architectural patterns.
– Strategic Thinking: Ability to align technical solutions with long-term business strategy.

Nice-to-Have Skills:

– Hands-on Coding Ability: Proficiency in Python/PySpark, allowing for the creation of architectural proofs-of-concept.
– DevOps & IaC Acumen: Deep understanding of CI/CD for data platforms and experience with Infrastructure as Code (Bicep/Terraform)/Experience with AzureDevOps for BigData services
– Azure Cost Management: Experience with FinOps and optimizing the cost of Azure data services.

Job responsibilities

– End-to-End Architecture Design: Design and document the complete, end-to-end data architecture, encompassing data ingestion, processing, storage, and analytics serving layers.
– Technology Selection & Strategy: Make strategic decisions on the use of Azure services (ADF, Databricks, Synapse, Event Hubs) to meet both immediate MVP needs and future scalability requirements.
– Define Standards & Best Practices: Establish data modeling standards, development best practices, and governance policies for the engineering team to follow.
– Technical Leadership: Provide expert technical guidance and mentorship to the data engineers and BI developers, helping them solve the most complex technical challenges.
– Stakeholder Communication: Clearly articulate the architectural vision, benefits, and trade-offs to technical teams, project managers, and senior business leaders.

What we offer

Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. 

Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.

Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.

Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!

High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.

Description

The GlobalLogic technology team is focused on next-generation health capabilities that align with the client’s mission and vision to deliver Insight-Driven Care. This role operates within the Health Applications & Interoperability subgroup of our broader team, with a focus on patient engagement, care coordination, AI, healthcare analytics, and interoperability. These advanced technologies enhance our product portfolio with new services while improving clinical and patient

experiences.

Requirements

An AWS Data Engineer designs, develops, and maintains scalable data solutions using AWS cloud services.

Key Responsibilities:
    • Design, build, and manage ETL (Extract, Transform, Load) pipelines using AWS services (e.g., Glue, Lambda, EMR, Redshift, S3).
    • Develop and maintain data architecture (data lakes, warehouses, databases) on AWS.
    • Implement data quality and governance solutions.
    • Automate data workflows and monitor pipeline health.
    • Ensure data security and compliance with company policies.
Required Skills:
    • Proficiency with AWS cloud services, especially data-related offerings (S3, Glue, Redshift, Athena, EMR, Kinesis, Lambda).
    • Strong SQL and Python skills.
    • Experience with ETL tools and frameworks.
    • Familiarity with data modelling and warehousing concepts.
    • Knowledge of data security, access management, and best practices in AWS.
Preferred Qualifications:
    • AWS certifications (e.g., AWS Certified Data Analytics – Speciality, AWS Certified Solutions Architect).
    • Background in software engineering or data science.

• Hands-on experience with Oracle Database and log-based Change Data Capture (CDC) replication using AWS Database Migration Service (DMS) for near real-time data ingestion.

Job responsibilities

– Develops, documents, and configures systems specifications that conform to defined architecture standards, address business requirements, and processes in the cloud development & engineering.

– Involved in planning of system and development deployment, as well as responsible for meeting compliance and security standards.

– API development using AWS services in a scalable, microservices-based architecture

– Actively identifies system functionality or performance deficiencies, executes changes to existing systems, and tests functionality of the system to correct deficiencies and maintain more effective data handling, data integrity, conversion, input/output requirements, and storage.

– May document testing and maintenance of system updates, modifications, and configurations.

– May act as a liaison with key technology vendor technologists or other business functions.

– Function Specific: Strategically design technology solutions that meet the needs and goals of the company and its customers/users.

– Leverages platform process expertise to assess if existing standard platform functionality will solve a business problem or if a customisation solution would be required.

– Test the quality of a product and its ability to perform a task or solve a problem.

– Perform basic maintenance and performance optimisation procedures in each of the primary operating systems.

– Ability to document detailed technical system specifications based on business system requirements

– Ensures system implementation compliance with global & local regulatory and security standards (i.e. HIPAA, SOCII, ISO27001, etc.)

What we offer

Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. 

Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.

Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.

Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!

High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.

Introduct Group is an international technology company delivering tailored software solutions and digital products for businesses worldwide. We work on complex, large-scale projects across multiple industries, helping clients build reliable, scalable, and secure systems.

Our company participates in a variety of projects across the Middle East, including the UAE, Saudi Arabia, and Oman, working with both government institutions and private sector clients.

We are currently looking for an experienced Data Engineer with Oracle experience to join an international project focused on modernizing data infrastructure and migrating an existing Data Warehouse environment to an Oracle Exadata platform.

The role involves long-term business trips or partial relocation to Oman.

Requirements

    •    5+ years of experience as a Data Engineer or working with data platforms

    •    Strong experience with Oracle databases (development, optimization, or administration)

    •    Experience with ETL processes and tools

    •    Experience with Pentaho or similar ETL solutions

    •    Experience working with large-scale data environments

    •    Understanding of data modeling principles (DWH concepts, schemas)

    •    Experience with database monitoring and performance optimization

    •    Basic understanding of networking concepts

    •    Strong troubleshooting and analytical skills

    •    English level: Upper-Intermediate or higher

Responsibilities

    •    Analyze the existing Oracle-based data environment, including data flows and dependencies

    •    Design and implement data pipelines for data migration and transformation

    •    Participate in the migration of Data Warehouse solutions to Oracle Exadata

    •    Develop, optimize, and maintain ETL processes

    •    Ensure data quality, consistency, and reliability across systems

    •    Monitor data pipelines and optimize performance of data processing workflows

    •    Collaborate with infrastructure and backend teams to ensure proper data integration

    •    Troubleshoot and resolve data-related issues in production environments

Nice to have

    •    Experience working with Oracle Exadata

    •    Experience with large-scale Data Warehouse (DWH) environments

    •    Experience with data migration projects

    •    Experience working with legacy data systems

    •    Basic knowledge of Linux administration

What we offer

    •    Competitive salary

    •    Participation in an international enterprise project

    •    Participation in a large-scale, national-level government project

    •    Business trips to Oman or partial relocation for on-site collaboration with the client and project team

    •    Accommodation provided

    •    Transportation cost compensation

We are looking for a Senior Data Engineer. This is not a standard ETL role. You will work on a high-load system processing 10,000+ executions per minute, building scalable pipelines where AI and LLM logic are embedded directly into production data flows.

The role combines data engineering, backend thinking, infrastructure ownership, and real production AI integration.

What you will do:

• Design and maintain scalable data pipelines in Python on Google Cloud Platform
• Build and optimize AI-driven scoring flows using LLMs for real-time identity enrichment
• Work with BigQuery, Apache Spark, and Apache Iceberg for heavy data processing
• Maintain high-load orchestration with custom Python nodes and complex workflows
• Own deployment and release processes (CI/CD)
• Optimize PostgreSQL performance under heavy load
• Investigate incidents and resolve root causes permanently

• Use AI engineering tools such as Claude in daily development

   

Requirements:

• 4+ years of experience in Data Engineering / Backend Engineering
• Strong Python skills
• Commercial experience with production LLM integration (OpenAI / Anthropic / Gemini)
• Strong knowledge of GCP (Cloud Functions, IAM, Networking)
• Experience with BigQuery, Spark, Iceberg
• Good understanding of Kubernetes
• Experience with workflow orchestration tools (n8n is a strong plus)
• Strong practical knowledge of PostgreSQL

• English level B2+

   

What is important for us:

• Ownership mindset
• Fast learning ability
• Strong problem-solving approach
• Active AI usage in engineering work
• Focus on practical results over unnecessary complexity

We are looking for a Senior Data Engineer who can design, implement, and evolve scalable data systems in AWS.

Core Responsibilities
 

• Design and implement batch and streaming data pipelines using Apache Spark.
• Build and evolve a scalable AWS-based data lake architecture.
• Develop and maintain real-time data processing systems (event-driven pipelines).
• Own performance tuning and cost optimization of Spark workloads.
• Define best practices for data modeling, partitioning, and schema evolution.
• Implement monitoring, observability, and data quality controls.
• Contribute to infrastructure automation and CI/CD for data workflows.
• Participate in architectural decisions and mentor other engineers.

Required Qualifications
 

• 5+ years of experience in Data Engineering.
• Strong hands-on experience with Apache Spark (including Structured Streaming).
• Experience building both batch and streaming pipelines in production environments.
• Proven experience designing AWS-based data lake architectures: S3, EMR, Glue, Athena.
• Experience with event streaming platforms such as Apache Kafka or Amazon Kinesis.
• Experience implementing lakehouse formats such as Delta Lake.
• Strong understanding of partitioning strategies and schema evolution.
• Experience using SparkUI and AWS CloudWatch for profiling and optimization.
• Strong understanding of Spark performance tuning (shuffle, skew, memory, partitioning).
• Proven track record of cost optimization in AWS environments.
• Experience with Docker and CI/CD pipelines.
• Experience with Infrastructure as Code: Terraform, AWS CDK.
• Familiarity with monitoring and observability practices.
• Experience in the Financial domain.
• Experience running Spark workloads on Kubernetes.
• Experience implementing data quality frameworks or metadata/lineage systems.