Jobs Data Engineer

Description

The Digital Health organization is technology team which focused on next generation Digital Health capabilities which deliver on the Medicine mission and vision to deliver Insight Driven Care. This role will operate within the Digital Health Applications & Interoperability subgroup of the broader Digital Health team, focused on patient engagement, care coordination, AI, healthcare analytics & interoperability amongst other advanced technologies which enhance our product portfolio with new services, while improving clinical & patient experiences.

The project is a cloud-based PaaS Ecosystem built with a privacy by design centric approach to provide a centralized cloud-based platform to store, classify, and control access to federated datasets in a scalable, secure, and efficient manner.

The ecosystem will allow Customer Operating Units (medical device departments) to store federated data sets of varying sizes and formats and control access to those data sets through Data steward(s). Source data sets can be exposed to qualified use cases and workflows through different project types.

The Healthcare Data Platform ecosystem will provide ML/AI project capabilities for streamlined development processes and a ML/AI workbench to enhance data exploration, wrangling, and model training.

In queue: 15+ OU’s. At this moment focused on – Nuero, Cardio, Diabetes is the OU that data platform is working with, but there could be more OU’s coming up with requirements in future.

GL Role: is to work on the enhancement of current capabilities, including taking over the work that AWS proserve team is doing, and develop new requirements that will keep coming from different OU’s in the future.

Requirements

MUST HAVE

• AWS Platform: Working experience with AWS data technologies, including S3, AWS RDS, Lake Formation
• Programming Languages: Strong programming skills in Python
• Data Formats: Experience with JSON, XML and other relevant data formats
• CI/CD Tools: Ability to deploy using established CI/CD pipelines using GitLab CI, Jenkins, Terraform or similar tools
• Scripting and automation: experience in scripting language such as Python, PowerShell, etc…
• Monitoring and Logging: Familiarity with monitoring & logging tools like CloudWatch, Splunk, ELK, Dynatrace, Prometheus
• Source Code Management: Expertise with GitLab
• Documentation: Experience with markdown and in particular Antora for creating technical documentation 

NICE TO HAVE

• Previous Healthcare or Medical Device experience
• Experience implementating enterprise grade cyber security & privacy by design into software products
• Experience working in Digital Health software
• Experience developing global applications
• Strong understanding of SDLC; experience with Agile methodologiesSoftware estimation
• Experience leading software development teams onshore and offshore
• Experience with FHIR

Job responsibilities

• Implement data pipelines using AWS services such as AWS Glue, Lambda, Kinesis, etc
• Implement integrations between the data platform and systems such as Atlan, Trino/Starburst, etc
• Complete logging and monitoring tasks through AWS and Splunk toolsets
• Develop and maintain ETL processes to ingest, clean, transform and store healthcare data from various sources
• Optimize data storage solutions using Amazon S3, AWS RDS, Lake Formation and other AWS technologies.
• Document, configure, and maintain systems specifications that conform to defined architecture standards, address business requirements, and processes in the cloud development & engineering.
• Participate in planning of system and development deployment as well as responsible for meeting compliance and security standards.
• Actively identify system functionality or performance deficiencies, execute changes to existing systems, and test functionality of the system to correct deficiencies and maintain more effective data handling, data integrity, conversion, input/output requirements, and storage.
• Document testing and maintenance of system updates, modifications, and configurations.
• Leverage platform process expertise to assess if existing standard platform functionality will solve a business problem or customization solution would be required.
• Test the quality of a product and its ability to perform a task or solve a problems.
• Perform basic maintenance and performance optimization procedures in each of the primary operating systems.
• Ensure system implementation compliance with global & local regulatory and security standards (i.e. HIPAA, SOCII, ISO27001, etc…)

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal assistant that uses hosted APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

Responsibilities:

• Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)
• Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests
• Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.
• Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history
• Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

Mandatory Skills:

8+ years of hands-on experience in Data Science and 2+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
Strong programming skills in languages such as Python and familiarity building ETL pipelines.
Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases
Solid experience with OpenSearch
Familiarity with AWS cloud platform and its services.
Experience with version control systems (e.g., Git) and CI/CD pipelines.
Ability to build scalable infrastructure to embed and search very large number of documents.
Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
Expertise in ML
Strong English skills (B2 and higher)
Strong verbal and written communication skills.
Ability to work independently and collaborate in a group.

Nice-to-Have Skills:

Agile certification
Oracle/Microsoft attestations and certifications
Domain knowledge - Trading and Capital Markets

About the Role

We are looking for a Cloud Platform Engineer with strong expertise in Azure Cloud and Kubernetes to join a growing engineering team. In this role, you will be responsible for designing, implementing, and supporting cloud-native platforms, workflow orchestration solutions, and event-driven architectures. You will work closely with international stakeholders to improve scalability, reliability, and automation across modern cloud environments.

Responsibilities

• Design, implement, and support cloud-native and containerized solutions
• Manage, optimize, and troubleshoot Kubernetes environments (AKS preferred)
• Develop and maintain workflow orchestration platforms and automation pipelines
• Build and support messaging and event-driven integration solutions
• Improve CI/CD processes, GitOps practices, and infrastructure automation
• Implement Infrastructure as Code (IaC) and cloud governance best practices
• Monitor platform health, performance, and reliability
• Collaborate with architects, developers, and business stakeholders in an international environment
• Contribute to platform improvements, operational excellence, and technical standards

Requirements

Must Have

• Strong hands-on experience with Kubernetes (AKS preferred)
• Solid experience with Microsoft Azure
• Experience with workflow orchestration tools such as:
  • Argo Workflows
  • Temporal
  • Apache Airflow
• Experience with messaging and integration technologies:
  • Kafka
  • Azure Service Bus
  • Azure Event Hub
• Strong Python skills for automation and scripting
• Experience with CI/CD pipelines and DevOps practices
• Knowledge of GitOps principles
• Experience with Infrastructure as Code (Terraform, Bicep, or similar)
• Experience with monitoring and observability solutions
• Understanding of cloud-native architecture and distributed systems
• Good communication skills and consultative mindset
• Upper-Intermediate English or higher

Nice to Have

• Experience with ArgoCD
• Experience with Helm
• Azure certifications
• Experience with microservices architecture
• Experience working in enterprise cloud environments
• Knowledge of security best practices in Kubernetes and Azure

What We Offer

• Opportunity to work on modern cloud-native platforms
• International and collaborative environment
• Challenging technical projects using modern technologies
• Long-term cooperation
• Remote work flexibility
• Professional growth and development opportunities

Technology Stack

Azure, AKS, Kubernetes, Python, Kafka, Azure Service Bus, Azure Event Hub, Argo Workflows, Temporal, Airflow, GitOps, CI/CD, Terraform, IaC, Monitoring & Observability

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal assistant that uses hosted APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

• Responsibilities:

  • Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)
  • Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests
  • Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.
  • Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history
  • Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

• Mandatory Skills Description:

  8+ years of hands-on experience in Data Science and 2+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
  Strong programming skills in languages such as Python and familiarity building ETL pipelines.
  Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases
  Solid experience with OpenSearch
  Familiarity with AWS cloud platform and its services.
  Experience with version control systems (e.g., Git) and CI/CD pipelines.
  Ability to build scalable infrastructure to embed and search very large number of documents.
  Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
  Expertise in ML
  Strong English skills (B2 and higher)
  Strong verbal and written communication skills.
  Ability to work independently and collaborate in a group.

• Nice-to-Have Skills Description:

  Agile certification
  Oracle/Microsoft attestations and certifications
  Domain knowledge - Trading and Capital Markets

The DataOS team at AppsFlyer is building and operating a reliable, high-scale data platform that powers analytics and product capabilities across the company.
We process more than 150 billion events per day and manage petabyte-scale historical data. Our team owns much more than a traditional “data lake” — including schema management, ETL pipelines, and interfaces to and from storage.

If you’re excited by scale, ownership, and building production-grade data systems, we’d love to meet you.

Tech stack highlights

Key technologies include:

• Processing and streaming: Spark, Kafka
• Data lake and formats: S3, Parquet, Delta Lake, Iceberg, Hadoop, Hive
• Cloud and compute: AWS, EMR/EMR Serverless, Athena
• Analytics and data platforms: BigQuery, BigLake, Aerospike, AWS Glue, Databricks Delta Engine
• Languages: Scala, Python (team focus), plus a broader polyglot engineering environment across AppsFlyer

What you’ll do:

• Design, build, and evolve large-scale data pipelines from raw ingestion to production-ready datasets.
• Develop and maintain Spark/Scala-based data processing workflows for both batch and streaming use cases.
• Own data platform reliability, quality, and performance in production environments.
• Collaborate with engineers, product owners, and stakeholders to define and deliver robust data solutions.
• Help shape engineering standards and operational practices as the DataOS team grows.

What You Bring:

• 3+ years of software engineering experience, with strong hands-on Scala and/or Java development.
• Strong Java core fundamentals.
• Proven experience with Spark and Kafka in production environments.
• Experience with the Hadoop ecosystem and distributed data processing platforms (AWS, GCP, Azure, CDP, MapR, or similar).
• Hands-on AWS experience, especially with S3 and EMR/EMR Serverless (Athena is a strong advantage).
• Experience building and operating cloud-based, high-scale distributed systems.
• Strong analytical and interpersonal skills, with the ability to work effectively across engineering and business teams.
• Professional spoken and written English.

Bonus Points:

• Knowledge of Scala is a plus.
• Experience with AWS authentication/authorization and cloud security fundamentals.
• Experience with CI/CD pipelines and tooling.
• Experience with DevOps and infrastructure-as-code tools (e.g., Terraform).
• Experience in SaaS product companies.
• Referred by an AppsFlyer employee.

Benefits:

• Health Insurance
• Paid Unlimited Vacation Days + all national holidays + additional recharge days
• Meals Reimbursement
• Sport Reimbursement
• Breakfast in the office
• Mental health program
• Team building, happy hours, and other team activities
• Paid sick days
• Snacks, fruits & ice cold beer
• All new team members are provided with a brand-new Mac laptop with 2 monitors and a Starter package

Our client is a neocloud building purpose-built GPU infrastructure for AI workloads. They operate large-scale clusters powering training and inference for some of the most demanding AI customers in the market, and they're rapidly expanding. Their infrastructure runs on NVIDIA and AMD accelerators, InfiniBand and high-speed Ethernet fabrics, and a production stack spanning bare-metal provisioning, Kubernetes, and OpenStack.

They're small, senior, and moving fast. The people who do well with our client own problems end-to-end and make decisions with incomplete information.

The role

We are hiring a Senior SRE to own the incident and problem management programs for our client. This is the person who runs the call when a customer cluster goes down, drives the post-mortem afterward, and hardens the runbooks so the same failure does not surface twice.

This is not an ITIL paperwork role. You will be technically credible across Kubernetes, Linux, networking, and GPU infrastructure, enough to lead a live incident with platform engineers, and a customer on the bridge. But your center of gravity is the process: fast, clean incident response, honest communication, and a relentless feedback loop between customers and engineering.

You will set the standard for how our client behaves during their most challenging hours. Done well, this is one of the most visible and highest-leverage roles on the team. You will work closely with cloud operations, network teams, and customer-facing counterparts. Expect to be on-call in a shared rotation.

What you'll own

• Incident command. Run major incidents end-to-end: triage, escalation, comms cadence, timeline, decision log, and drive-to-resolution. You are the conductor, not the fixer.
• Customer communications. Own customer-facing incident messaging, initial acknowledgement, status updates at defined intervals, resolution notice, and written RCA. Clear, honest, no hedging.
• Post-mortem and RCA program. Run blameless post-mortems, write the RCA, track action items to completion, and publish learnings across the org. Own the quality bar for RCA writing for our client.
• Escalation framework. Define and maintain the severity matrix, escalation trees, on-call rotations, and paging policies across platform, network, DC ops, and leadership.
• Runbooks and playbooks. Turn tribal knowledge into durable, testable runbooks. Drive the engineering teams to document before the next 2am page, not after.
• On-call health. Own the on-call experience: page volume, alert quality, rotation fairness, and handoff hygiene. Kill noisy alerts. Fix the ones that matter.
• Reliability metrics. Define and report the metrics that matter: MTTA, MTTR, incident count by class, SLO attainment, repeat-offender systems. Make reliability a number the business trusts.
• Game days and drills. Plan and run incident drills across clusters, regions, and customer scenarios. Surface gaps before customers do.
• Incident tooling. Own the incident tooling stack, paging, status page, incident channel automation, timeline capture, RCA templates. Evaluate and land the right tools.

What we're looking for

Required

• 7+ years in SRE, production engineering, or infrastructure operations, with clear ownership of incident management in at least one prior role.
• Proven ability to run major incidents as the commander, keeping a room calm, driving decisions, managing comms to customers and executives in parallel.
• Strong technical fluency across Linux, Kubernetes, networking ($L2/L3$, BGP basics), and cloud or bare-metal infrastructure.
• Deep enough in the stack to challenge assumptions, debug alongside engineers on a bridge call, and senior enough to turn each incident into durable improvements like runbooks, automation, guardrails, so the next incident does not need you.
• Excellent written communication. You can draft a customer-facing RCA that is accurate, clear, and does not over- or under-promise, and do it within the SLA window.
• Experience owning SLOs, error budgets, or equivalent reliability metrics as a working practice, not a slide.

• Experience designing on-call rotations and alerting practices across multiple teams. Comfortable operating in a fast-moving, still-forming environment. You write the runbook that did not exist yesterday.

   

Strongly preferred

• Experience in GPU infrastructure, HPC, or AI/ML platform operations, NCCL, InfiniBand, DCGM, or similar.
• Direct experience with Kubernetes incident response, control plane, CNI, operator patterns, noisy-neighbor debugging.
• Experience interfacing with neocloud, enterprise or hyperscaler customers during live incidents.
• Familiarity with Prometheus, Checkmk, Grafana, and modern incident tooling (OpsGenie/JIRA).
• Prior experience at a neocloud, CSP, or infrastructure vendor where downtime had direct revenue impact.

How we work

• We're remote-first across US, LATAM, and EU time zones with a strong operational culture.
• We use AI tooling heavily in day-to-day operations and expect everyone on the team to be fluent with it and to help us get more leverage from it.
• We ship, we debug, we iterate. We don't process-engineer our way around problems that need to be solved.
• We value written artifacts, RCAs, runbooks, design docs, over meetings. We value calm over performative urgency.
• We protect focus time and we protect sleep.

AI as a force multiplier

Our client runs the infrastructure that powers the AI economy. We expect our team to use AI aggressively in how we work. For this role specifically, that means using AI to draft RCAs faster, triage logs and metrics during incidents, summarize timelines, generate customer-facing communications from raw incident data, and turn post-mortems into runbook updates automatically. If you have not yet built this into your workflow, you will here.

What success looks like

First 30 days

• Shadowed on-call rotations across the platform, network, and ops teams.
• Read every RCA written and identify the top three recurring failure classes.
• Met with the top three customers and understood their incident expectations and SLA terms.
• Documented the current-state incident response process, gaps and all.

First 90 days

• Published a revised severity matrix, escalation tree, and customer-comms cadence, adopted across teams.
• Run at least two major incidents as commander with clean timelines and customer comms.
• Delivered a new RCA template and coached the team through at least three post-mortems against it.
• Stood up baseline reliability metrics (MTTA, MTTR, incident count by class) with a weekly reporting cadence.
• Killed at least 20% of current alert volume by eliminating low-signal pages.

`First 12 months

• Our client has a mature, documented incident management program that a new customer can diligence with confidence.
• Repeat-offender incident classes are down materially, with clear attribution to runbook, tooling, or engineering changes you drove.
• Customer-facing RCAs are consistently delivered within SLA and are referenced by customers as a differentiator.
• On-call health (page volume, rotation fairness, engineer-reported burden) has measurably improved.
• Game days are a regular cadence and have surfaced and closed real gaps before they became customer incidents.

About the project

Our client is a global company with a platform managing over 700,000 hotel rooms across 124 countries, helping clients generate billions in revenue.

Why join

— Stable long-term cooperation: already 5 engineers are working on the project
— Technical freedom: ability to propose architecture decisions and improvements without heavy bureaucracy
— End-to-end ownership: work with the full data pipeline from raw data to production-ready solutions
— Direct business impact: your work helps increase hotel revenues
— Collaboration with major global hotel brands
— Fast and measurable results with clear business value

Requirements

Must have

— 5+ years of experience in Data Engineering
— 3+ years of commercial experience with AWS (including at least 1 year on the latest project)
— 2+ years of experience with Spark/PySpark (including at least 1 year on the latest project)
— Strong experience with:

• Spark / PySpark
• AWS S3
• Kafka or Kinesis
• DynamoDB
• PostgreSQL or MySQL
• AWS ECS Fargate

Nice to have

• AWS Glue
• EMR
• AWS Batch
• Flink
• Apache Beam
• Cassandra / ScyllaDB
• AWS Step Functions

Responsibilities

• Implement scaling of the case detection process (scanner)
• Split monolithic API into multiple services
• Emulate Pub/Sub and Cloud Run locally
• Extract and process data using PySpark + S3
• Work with Kafka/Kinesis pipelines (as a plus)
• Write and optimize SQL queries in PostgreSQL/Redshift
• Use DynamoDB as a data source and sink
• Run and debug ETL jobs on AWS Glue or EMR
• Monitor jobs via AWS Console and Spark UI

We are looking for an experienced Data Engineer to design and maintain robust data infrastructure across our systems. In this role, you will be responsible for building scalable data pipelines, ensuring data integrity, and integrating third-party data sources. Your primary focus will be to enable efficient data flow and support analytical capabilities across the organization. You will also contribute to the development of our data architecture, implement best engineering practices, and collaborate closely with cross-functional teams to turn raw data into actionable insights.

Requirements

• 3+ years of commercial experience in Data Engineering
• Experience gathering business requirements and communicating with stakeholders
• Strong knowledge of Data Warehouse (DWH) architecture and principles
• Hands-on experience designing data warehouses and building ETL pipelines
• Deep practical experience with Python
• Hands-on experience in one or more of the following areas: data processing using PySpark, Apache Spark, Pandas, or similar frameworks; data scraping; integration of third-party data sources and APIs
• Proficiency in SQL and relational databases such as PostgreSQL and MySQL
• Hands-on experience with column-oriented databases such ClickHouse, Snowflake, Cassandra, BigQuery (many tables with millions of records and above each)
• Solid understanding of software design patterns, algorithms, and data structures
• Intermediate English proficiency

Will be a plus

• Experience with messaging platforms such as RabbitMQ or Kafka
• Familiarity with DataOps practices and data pipeline automation
• Familiarity with CI/CD pipelines and Kubernetes environments
• Background in FinTech or Trading domains

Responsibilities

• Communicate with technical and non-technical stakeholders to gather and clarify business requirements
• Review and analyze data and business logic to ensure consistency, accuracy, and reliability
• Design, implement, and maintain data pipelines for efficient and scalable data processing
• Integrate, maintain, and support data solutions and third-party data sources
• Research and evaluate third-party tools, services, and components for potential use
• Follow engineering best practices, including code reviews, testing, refactoring, and continuous delivery
• Design, optimize, and maintain data storage solutions

What we offer:

• Tax expenses coverage for private entrepreneurs in Ukraine
• Expert support and guidance for Ukrainian private entrepreneurs
• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

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.

- 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

Must-have skills:
- Data engineering - 5 years, 
- AWS - 3 years (1 year latest project), 
- Spark/PySpark - 2 years (1 year latest project)
- English B2+

About Traffic Label

Traffic Label is a fast-growing, data-driven technology company operating in the iGaming and affiliate marketing space, building scalable products focused on performance, analytics, and automation across global markets.

Role Overview

We are looking for a Data Engineer (integration) to join our Data Platform team to help build, maintain, and scale integrations between external platforms and internal systems. Reporting directly to the Data Engineering Lead, you will focus on reliable backend services, AWS server-less infrastructure, and external API integrations that support critical business operations.
This role is fully remote across Europe and sits within the Development Department - in the Data Platform team/squad.
We are open to Seniority level here from Mid level and above.

Key Responsibilities

• Design, develop, and maintain integrations between third-party platforms and internal systems.
• Build scalable backend services and API-based integration solutions using Python.
• Manage integrations across AWS server-less infrastructure including Lambda, S3, DynamoDB, EventBridge, and related services.
• Implement and maintain authentication flows, token refresh handling, retries, pagination, polling, and async job processing.
• Monitor, troubleshoot, and resolve integration issues in production environments.
• Own integrations end-to-end, including implementation, deployment, operations, and incident handling.
• Write and maintain automated tests to ensure reliability and stability of integration services.
• Collaborate closely with Engineering, Product, and Data teams to support platform scalability and operational efficiency.
• Contribute to infrastructure improvements and Infrastructure-as-Code initiatives.

Skills & Experience

• 4+ years of backend or integration engineering experience in production environments.
• Strong Python development skills with experience building and maintaining external API integrations.
• Hands-on AWS experience including Lambda, S3, DynamoDB, EventBridge, Secrets Manager, SSM Parameter Store, and CloudWatch.
• Experience with Infrastructure-as-Code for provisioning and maintaining cloud infrastructure.
• Strong understanding of REST APIs and integration patterns.
• Experience handling authentication flows, retries, pagination, polling, and asynchronous processing.
• Strong debugging and troubleshooting skills, particularly with third-party integrations.
• Experience writing and maintaining automated tests for backend or integration services.
• Ability to work independently and take ownership of technical deliverables.
• At least 6 months of hands-on experience using AI coding agents in day-to-day development workflows.
• Experience with Airflow or similar workflow orchestration tools is considered a plus.
• Experience working with ESP or CRM platforms is considered an advantage.
• Familiarity with affiliate marketing or iGaming environments is a plus.
• Understanding of marketing data structures and identifiers is considered beneficial.

What We Offer

• Competitive salary.
• Fully remote work across Europe.
• Flexible collaboration hours and strong work-life balance.
• The opportunity to work on scalable, business-critical systems and integrations.
• A collaborative culture focused on ownership, innovation, and continuous improvement.