Jobs Data Engineer

Duration: 3–5 months
Location: Armenia, Ukraine, Poland, Estonia
 

Responsibilities:

• Shape and contribute to Cargoo’s data engineering strategy, including the design, architecture, and implementation of scalable data solutions
• Build, maintain, and evolve the Data Platform to support advanced analytics, machine learning, and real-time decision-making
• Collaborate closely with AI engineers, BI analysts, and business stakeholders to translate data needs into robust technical solutions
• Identify data opportunities across the organization and enable teams to extract maximum value from data assets
• Design and implement ETL/ELT pipelines, including batch and streaming data workflows
• Develop and maintain data models using dimensional and advanced modeling techniques
• Support deployment, monitoring, and lifecycle management of data applications
• Apply DataOps best practices to ensure reliability, scalability, and quality of data pipelines
• Participate in code reviews and promote high engineering standards
• Take ownership of projects end-to-end, ensuring timely delivery and measurable impact
   

Requirements:

• Solid proficiency in Python (runtime environment, package management) and SQL (DML, DDL)
• Hands-on experience with SQL Server / Azure SQL Server
• Experience working with cloud platforms, preferably Microsoft Azure
• Familiarity with the modern data stack, including tools such as dbt and orchestration frameworks (Airflow, Dagster, or similar)
• Strong understanding of ETL/ELT concepts, data architecture, and data modeling
• Experience with streaming technologies (Kafka or equivalent)
• Experience with Docker and container orchestration technologies
• Experience deploying and monitoring applications on Kubernetes (K8s)
• Knowledge of application lifecycle management
• Understanding and application of DataOps practices
• Strong project management, execution skills, and a clear sense of ownership and accountability

On behalf of our client, we are looking for a Senior Data Engineer.

About the client: 

The company helps hotel chains like Hilton and Marriott transform front-desk staff into revenue generators through upselling training and analytics. Their IN-Gauge SaaS platform empowers frontline teams across 2,000+ properties in over 100 countries.
 

Key Responsibilities: 
- Implement scaling of the case detection process (scanner)
- Split API into multiple API services
- Emulate pubsub/cloud run locally
- Extract & process data using PySpark + S3 (Kafka/Kinesis — bonus)
- Write & optimize SQL on PostgreSQL/Redshift; use DynamoDB as data source/sink
- Run and debug ETL jobs on Glue or EMR; monitor via console/Spark UI

Company offers:

- Long-term employment with possibilities for professional growth

- Fully remote work

- Reasonably flexible schedule

- 15 days of paid vacation

- Regular performance reviews

🚀 Lead Data Engineer (EU)

We’re looking for a Lead Data Engineer to join a high-impact data transformation initiative focused on modernizing data pipelines and enabling scalable, future-ready data architecture.

This role is ideal for someone who enjoys working на стику data engineering, architecture, and cross-team collaboration, and хоче впливати на стратегічні рішення у data ecosystem.

🌍 About the Project

You will be part of a large-scale initiative aimed at transforming how data is ingested, processed, and consumed across the organization.

The key focus is on migrating and optimizing data flows, ensuring seamless integration with modern platforms such as Databricks Unity Catalog, and enabling more efficient and scalable data consumption.

🧩 Key Responsibilities

• Analyze existing data pipelines and codebase to identify data entry points
• Design and execute data source migration strategies (source-to-target mapping, adapters development)
• Redesign data consumption processes to support integration with Databricks Unity Catalog
• Collaborate closely with cross-functional teams (data foundation & product teams)
• Ensure smooth transition and alignment across systems, including permissions and dependencies
• Drive best practices in data engineering and contribute to architectural improvements

🛠️ Tech Stack

• Python / Spark / SQL
• Azure Data Factory
• Apache Airflow
• Databricks ecosystem (Unity Catalog)

Project description:
The goal of the project is to onboard the commercial data into EDF and deliver the reporting platform through PowerBI on top of Golden Layer built by GD (Medallion architecture).

Client description:
It is a global food and beverage leader with a product portfolio including 22 brands that generate more than $1 billion each in annual retail sales.

We're looking for a Data Engineer to:
Design and implement scalable data pipelines using Azure Data Factory and Databricks (PySpark).
Develop the Gold layer (semantic layer) for accurate business reporting.
Utilize Azure DevOps for CI/CD and implement observability to ensure data integrity.

Required Technology Stack:
Cloud: Azure
Processing: Databricks, PySpark
Storage: ADLS, Delta Lake
Orchestration: Azure Data Factory (ADF)
DevOps: Azure DevOps
Reporting: Power BI
Concepts: Medallion Architecture, Observability

About the Project

We are building a system that integrates multiple databases and external APIs, aggregates data from various sources, and constructs a real-time knowledge graph to track complex relationships between entities.

Key Requirement

‼️ Hands-on experience with graph databases (minimum 6 months) is mandatory.
We are specifically looking for candidates who have practical experience with graph data modeling and writing queries.

Tech Stack

Graph Databases, Neo4j, Python, FastAPI, SQL, NoSQL, Docker, Docker Compose.

Responsibilities

— Design, build, and maintain scalable data pipelines
— Develop and optimize graph-based data models (nodes, edges, properties)
— Build and maintain a real-time knowledge graph
— Integrate multiple data sources (APIs, SQL, NoSQL databases)
— Optimize graph queries and data processing performance
— Work with cloud infrastructure (AWS)
— Collaborate with backend and product teams to design data solutions
— Build proof-of-concept solutions involving graph technologies

Requirements

— 3+ years of experience as a Data Engineer / Backend Engineer (data-heavy projects)
— Hands-on experience with graph databases (Neo4j, ArangoDB, Amazon Neptune, etc.)
— Strong knowledge of SQL and NoSQL databases
— Experience building data pipelines and working with large datasets
— Good understanding of data modeling (especially graph structures)
— Experience with AWS or other cloud platforms
— Python proficiency
— English (from Upper-Inermediate)

Nice to Have

— Experience with knowledge graphs
— Experience with streaming or real-time data processing
— Background in Computer Science, Mathematics, or related field
— Experience with recommendation systems / analytics / fraud detection

Who will succeed in this role

Someone who enjoys working with complex data relationships, understands how to model real-world systems as graphs, and can bridge backend engineering with data engineering practices.

Our Client is the fast-paced digital healthcare company which creates the growing portfolio of online health communities for people with chronic conditions. Company’s mission is to improve patient’s quality of life by connecting them with each other, with caregivers and healthcare industry partners, building beneficial social interactions and meaningful health conversations. 

About project:
The core of the project is working with the data; the target is to re-design and enhance data-driven solutions. The team is responsible for expanding and optimizing the data flow and data pipeline architecture, model and analyze data, interpret trends or patterns in complex data sets and translate them into product and marketing insights. If you are excited by the prospect of optimizing or even re-designing our data architecture to support our next generation of products and data initiatives, we would be thrilled to have you apply!

Requirements:

● Good Python skills

● Good working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases

● A successful history of manipulating, processing and extracting value from large disconnected datasets

● Experience building and optimizing data pipelines, architectures and data sets

● Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement

● Strong analytic skills related to working with unstructured datasets

Technologies:

• Python
• AWS: SES, RDS (MySQL), EC2, Elasticsearch Service, Route 53, VPC, etc
• Snowflake
• Docker
• dbt
• Airflow
   

Job responsibilities:

● Manage and optimize core data infrastructure

● Develop custom data infrastructure not available off-the-shelf

● Build monitoring infrastructure to give visibility into the pipeline’s status

● Monitor all jobs for impact on cluster performance

● Run maintenance routines regularly

● Tune table schemas to minimize costs and maximize performance

● Build and maintaining custom ingestion pipelines

● Build non-SQL transformation pipelines

● Build processes supporting data transformation, data structures, metadata, dependency and workload management

Duration: 3–5 months
Location: Armenia, Ukraine, Poland, Estonia
 

Responsibilities:

• Shape and contribute to Cargoo’s data engineering strategy, including the design, architecture, and implementation of scalable data solutions
• Build, maintain, and evolve the Data Platform to support advanced analytics, machine learning, and real-time decision-making
• Collaborate closely with AI engineers, BI analysts, and business stakeholders to translate data needs into robust technical solutions
• Identify data opportunities across the organization and enable teams to extract maximum value from data assets
• Design and implement ETL/ELT pipelines, including batch and streaming data workflows
• Develop and maintain data models using dimensional and advanced modeling techniques
• Support deployment, monitoring, and lifecycle management of data applications
• Apply DataOps best practices to ensure reliability, scalability, and quality of data pipelines
• Participate in code reviews and promote high engineering standards
• Take ownership of projects end-to-end, ensuring timely delivery and measurable impact
   

Requirements:

• Solid proficiency in Python (runtime environment, package management) and SQL (DML, DDL)
• Hands-on experience with SQL Server / Azure SQL Server
• Experience working with cloud platforms, preferably Microsoft Azure
• Familiarity with the modern data stack, including tools such as dbt and orchestration frameworks (Airflow, Dagster, or similar)
• Strong understanding of ETL/ELT concepts, data architecture, and data modeling
• Experience with streaming technologies (Kafka or equivalent)
• Experience with Docker and container orchestration technologies
• Experience deploying and monitoring applications on Kubernetes (K8s)
• Knowledge of application lifecycle management
• Understanding and application of DataOps practices
• Strong project management, execution skills, and a clear sense of ownership and accountability

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.

Requirements

Total of 5+ years of development/design experience with a minimum of 5 years experience in Big Data technologies on-prem or on cloud.
Experience with architecting, building, implementing and managing Big Data platforms On Cloud, covering ingestion (Batch and Real time), processing (Batch and Realtime), Polyglot Storage, Data Analytics and Data Access
Good understanding of Data Governance, Data Security, Data Compliance, Data Quality, Meta Data Management, Master Data Management, Data Catalog
Proven understanding and demonstrable implementation experience of big data platform technologies on cloud (AWS and Azure) including surrounding services like IAM, SSO, Cluster monitoring, Log Analytics etc
Experience working with Enterprise Data Warehouse technologies, Multi-Dimensional Data Modeling, Data Architectures or other work related to the construction of enterprise data assets
Strong Experience implementing ETL/ELT processes and building data pipelines including workflow management, job scheduling and monitoring
Experience building stream-processing systems, using solutions such as Apache Spark, Databricks, Kafka etc…
Experience with Spark/Databricks technology is a must
Experience with Big Data querying tools
Solid skills in Python
Strong experience with data modelling and schema design
Strong SQL programming background
Excellent interpersonal and teamwork skills
Experience to drive solution/enterprise-level architecture, collaborate with other tech leads
Strong problem solving, troubleshooting and analysis skills
Experience working in a geographically distributed team
Experience with leading and mentorship of other team members
Good knowledge of Agile Scrum
Good communication skills

 #LI-OE1

#LI-Remote

Job responsibilities

Work directly with the Client teams to understand the requirements/needs and rapidly prototype data and analytics solutions based upon business requirements
Design, implement and manage large scale data platform/applications including ingestion, processing, storage, data access, data governance capabilities and related infrastructure
Support Design and development of solutions for the deployment of data analytics notebooks, tools, dashboards and reports to various stakeholders
Communication with Product/DevOps/Development/QA team
Architect data pipelines and ETL/ELT processes to connect with various data sources
Design and maintain enterprise data warehouse models
Take part in the performance optimization processes
Guide on research activities (PoC) if necessary
Manage cloud based data & analytics platform
Establishing best practices with CICD under BigData scope

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.

Project Overview

The company helps hotel chains like Hilton and Marriott transform front-desk staff into revenue generators through upselling training and analytics. Their IN-Gauge SaaS platform empowers frontline teams across 2,000+ properties in over 100 countries.

Responsibilities

- Implement scaling of the case detection process (scanner)

- Split API into multiple API services

- Emulate pubsub/cloud run locally

- Implement scaling of the case detection process (scanner)

- Split API into multiple API services

- Emulate pubsub/cloud run locally

- Extract & process data using PySpark + S3 (Kafka/Kinesis — bonus)

- Write & optimize SQL on PostgreSQL/Redshift; use DynamoDB as data source/sink

- Run and debug ETL jobs on Glue or EMR; monitor via console/Spark UI

Requirements (Must-have)

Data engineering - 5 years,

AWS - 3 years (1 year latest project),

Spark/PySpark - 2 years (1 year latest project)

Nice to Have

GCP (pubsub/Cloud Run)

Hiring Process

Intro call

Technical discussion (focused on real experience)

Offer

Start: ASAP

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.

Skills requirements:
• 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;
• Knowledge of data algorithms and data structures is a MUST;
• Working with high volume tables 10m+.

Optional skills (as a plus):
• Experience with Spark (pyspark);
• Experience with Airflow;
• Experience with Kafka;
• Experience in statistics;
• Knowledge of DS and Machine learning algorithms..

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

We offer:

• 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.

Description

Client is a luxury skincare and beauty brand. The brand is based in San Francisco and sells luxury skincare products worldwide.

Tatcha’s main IT “product” is its e-commerce website, which functions as a digital platform to sell products, educate customers, and personalize experiences.

• Runs on Salesforce Commerce Cloud (formerly Demandware) — an enterprise e-commerce platform that supports online shopping, order processing, customer accounts, and product catalogs.
• Hosted on cloud infrastructure (e.g., AWS, Cloudflare) for reliable performance and security
  Uses HTTPS/SSL encryption to secure data transfers.
• Integrated marketing and analytics technologies such as Klaviyo (email & SMS automation), Google Tag Manager, and personalization tools to track behavior, optimize campaigns, and increase conversions

It’s both a shopping platform and a digital touchpoint for customers worldwide.

Requirements

• 6+ years of experience as a Data Engineer, Analytics Engineer, or in a similar data-focused role.
• Strong SQL skills for complex data transformations and analytics-ready datasets.
• Hands-on experience with Python for data pipelines, automation, and data processing.
• Experience working with cloud-based data platforms (AWS preferred).
• Solid understanding of data warehousing concepts (fact/dimension modeling, star schemas).
• Experience building and maintaining ETL/ELT pipelines from multiple data sources.
• Familiarity with data quality, monitoring, and validation practices.
• Experience handling customer, transactional, and behavioral data in a digital or e-commerce environment.
• Ability to work with cross-functional stakeholders (Marketing, Product, Analytics, Engineering).

Nice to have:

• Experience with Snowflake, Redshift, or BigQuery.
• Experience with dbt or similar data transformation frameworks.
• Familiarity with Airflow or other orchestration tools.
• Experience with marketing and CRM data (e.g. Klaviyo, GA4, attribution tools).
• Exposure to A/B testing and experimentation data.
• Understanding of privacy and compliance (GDPR, CCPA).
• Experience in consumer, retail, or luxury brands.
• Knowledge of event tracking and analytics instrumentation.
• Ability to travel + visa to the USA
•  

Job responsibilities

• Design, build, and maintain scalable data pipelines ingesting data from multiple sources:
  e-commerce platform (e.g. Salesforce Commerce Cloud), CRM/marketing tools (Klaviyo), web analytics, fulfillment and logistics systems.
• Ensure reliable, near-real-time data ingestion for customer behavior, orders, inventory, and marketing performance.
• Develop and optimize ETL/ELT workflows using cloud-native tools.
• Model and maintain customer, order, product, and session-level datasets to support analytics and personalization use cases.
• Enable 360° customer view by unifying data from website interactions, email/SMS campaigns, purchases, and returns.
• Support data needs for personalization tools (e.g. product recommendation quizzes, ritual finders).
• Build datasets that power marketing attribution, funnel analysis, cohort analysis, and LTV calculations.
• Enable data access for growth, marketing, and CRM teams to optimize campaign targeting and personalization
• Ensure accurate tracking and validation of events, conversions, and user journeys across channels.
• Work closely with Product, E-commerce, Marketing, Operations, and Engineering teams to translate business needs into data solutions.
• Support experimentation initiatives (A/B testing, new digital experiences, virtual stores).
• Act as a data partner in decision-making for growth, CX, and operational efficiency.
• Build and manage data solutions on cloud infrastructure (e.g. AWS).
• Optimize storage and compute costs while maintaining performance and scalability.

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.

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

   

• 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.

   

• Languages:
  • English: C1 Advanced

We are seeking a Middle Data Engineer to join our AI-Native Delivery Pod. In this role, you will move beyond traditional ETL development to become an "Architect of Data Flows," leveraging AI agents to design, build, and test complex data pipelines.

Your focus will be on data-intensive, No-UI projects where the primary value lies in the integrity, speed, and structure of the data. You will use Claude Code, Cursor, and agentic workflows to automate routine SQL and Python development, allowing you to concentrate on logic validation, data quality assurance, and performance optimization within cloud environments. As part of an AI-Native pod, you will operate at a velocity significantly higher than traditional teams by treating AI as your primary executor.

Required Qualifications

• Degree: Bachelor’s degree in Computer Science, Applied Mathematics, Data Engineering, or a related technical field.
• Professional Experience: 3+ years of experience in Data Engineering, specifically building and maintaining production-grade data pipelines.
• Tech Stack Mastery: Strong proficiency in Python (Pandas, PySpark, Boto3) and advanced SQL (window functions, optimization, procedural SQL).
• Data Tooling: Hands-on experience with orchestration tools (e.g., Airflow, Prefect, or Dagster) and data transformation frameworks like dbt.
• Cloud Ecosystem: Practical experience with cloud data warehouses and services, specifically within the Azure ecosystem (Azure Data Factory, Synapse, Databricks, or ADLS).
• AI-Native Proficiency: Proven experience using Generative AI tools (Claude Code, GitHub Copilot) to accelerate coding, migrations, and documentation.
• Data Quality & Testing: Solid understanding of data testing principles and experience implementing automated Data Quality Gates.
• Methodology: Familiarity with Spec-Driven Development, where code is generated based on formalized requirements and pre-defined test cases.

Preferred Qualifications

• Agentic Workflows: Experience building or using agentic systems to automate data processing or metadata management.
• Big Data Frameworks: Experience with distributed processing at scale (e.g., Spark, Flink, or Kafka).
• DataOps: Experience with containerization (Docker, Kubernetes) and building CI/CD pipelines specifically for data workloads.
• Systematic Debugging: Ability to apply a structured approach to debugging non-linear errors in massive datasets.

Job Responsibilities

• Pipeline Orchestration: Design and implement scalable ETL/ELT processes, piloting AI agents to generate boilerplate code and optimize complex queries.
• Agentic Execution: Use Claude Code CLI to implement data transformations, automate schema migrations, and generate pipeline documentation.
• Deep Review: Conduct rigorous analysis of AI-generated code, focusing on data lineage, cost-efficiency of cloud resources, and security.
• Data Quality Assurance: Define and implement automated "Quality Gates" to ensure data integrity and correctness at every stage of the pipeline.
• Context Hygiene: Maintain the project’s operational memory (the CLAUDE.md file and /docs folder) to ensure AI agents remain effective and aligned with project goals.
• Skill Library Contribution: Help build and refine the pod’s "Skills" library for Claude Code, automating recurring data engineering patterns.
• Stakeholder Collaboration: Work closely with the AI Solution Architect to implement data models and with the AI Reliability Engineer to integrate data into the broader project ecosystem.

Department/Project Description

You will join a specialized AI-Native Delivery Pod focused on complex, high-stakes Data Engineering projects. Our mission is to build robust data infrastructures for enterprise clients using a Fixed Price model, delivering results with unprecedented speed through the synergy of human expertise and agentic execution. Projects typically involve building modern Data Warehouses (DWH), Analytics Lakes, and AI-ready data foundations within the Azure cloud. We operate in an environment of high autonomy where every engineer is an operator of cutting-edge AI technology.

Skill Category

AI/ML

Keyskills - Must Have

• Python
• claude
• Data Engineer