AI Data Engineer

Nortiksoftware; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Remote Europe

Vacation days

Private health insurance

Learning budget

Research time

Hardware & AI tools

Mentorship from senior engineers

Referral bonus

Job summary

Nortiksoftware is hiring a data engineer who operates at the architecture level and also builds. You’ll shape and modernize client data platforms, from ingestion to dashboards, enabling AI workloads with scalable pipelines and governance.

You’ll work across Azure, AWS, GCP, and on-prem environments, designing batch and streaming architectures, and delivering BI reporting with Power BI, Superset, Grafana, or Tableau. This role emphasizes multi-cloud readiness and security compliance.

Qualifications

• Experience designing data platforms and pipelines end-to-end.

Responsibilities

• Design the data platform architecture, ingestion, storage, processing, and consumption layers.

Skills

Data architecture

Multi-cloud

Batch & streaming

English communication

Tools

Databricks

Snowflake

Iceberg

Power BI

Superset

Grafana

Tableau

Airflow

dbt

Kafka

Flink

Job description

We're the AI engineering partner enterprises bring in when the work has to be right the first time: regulated data, production load, and systems that have been running for a decade before we arrive. You'll ship to production in weeks rather than quarters, own what you build end to end, and work beside a senior team that has already done it at that scale.

We're looking for a data engineer who works at the architecture level and still builds. You'll design the data platforms our AI systems run on, from ingestion to the dashboards a client's leadership reads, and you'll modernise the legacy systems those platforms have to grow out of. If you've drawn the target architecture, picked the stack, and then stayed to make the pipelines hold under real volume, you'll recognise the job.

Most of our engagements now end in AI, and most of them start with data that isn't ready for it. The person who assesses that readiness, defines the platform that fixes it, and proves it with a working proof of concept is the person we're hiring. You don't need to have trained a model. You do need to know exactly what a model needs from the data underneath it, and how to build that across Azure, AWS, GCP, and on-premises environments.

What will be your responsibilities?

You'll own the shape of a client's data platform. That starts with assessing what they have: proposing changes to current data systems, including migration paths and modernisation strategies for legacy architectures, and assessing AI and ML readiness across data availability, quality, governance, and infrastructure constraints. From there you define and evolve the target architecture, covering the ingestion, storage, processing, and consumption layers, and translate business and product requirements into technical architecture, data models, and implementation blueprints the team can build from.

Then you build it. You'll design and implement both batch and streaming or event-driven architectures to match the processing requirements in front of you, design scalable, high-throughput pipelines that handle large data volumes at low latency, and architect and deliver the reporting systems on top of them in Power BI, Apache Superset, Grafana, or Tableau. You'll evaluate and recommend technology stacks across Azure, AWS, GCP, and on-premises environments for hybrid and multi-cloud deployments, and lead proof-of-concept design, implementation, and validation, including the architecture documentation that goes with it.

Throughout, you'll keep every deployment environment aligned with security, compliance, and regulatory standards, including GDPR, SOC 2, ISO 27001, HIPAA, and PCI-DSS. You'll collaborate closely with product managers, engineers, and domain experts, and you'll provide the technical guidance during the transition from architecture to implementation, staying with the system until it's carrying real load.

This position is made for you, if you:

• Have designed robust data systems from scratch and stayed accountable for them after launch: greenfield architecture definition, technology selection, and end-to-end implementation planning.
• Have modernised a legacy data system while it kept running, and know which parts of that are engineering and which are diplomacy.
• Have hands-on experience with high-throughput data systems and performance optimisation at scale, not just the diagrams of them.
• Can bridge business and technical stakeholders and communicate an architecture decision clearly to both.
• Think in tradeoffs: batch or streaming, lakehouse or warehouse, managed or self-run, and can say which and why.
• Treat governance and compliance as part of the design rather than a review gate at the end.
• Handle ambiguity as the default state of a new engagement, and know which questions collapse it fastest.
• Communicate clearly in English, in writing especially. We're remote, so decisions live in documents and threads.
• Give and take direct feedback without it becoming personal.
• Multi-cloud experience across Azure, AWS, and GCP, including hybrid and on-premises deployments.
• Experience with AI and ML production systems, and with the data readiness work that has to happen before them.
• Experience implementing lakehouse architecture with Apache Iceberg.
• Experience with modern data platforms such as Databricks and Snowflake.
• Solid understanding of batch architectures (ETL/ELT, data warehouses) and streaming architectures (Kafka, Flink, or equivalent).
• Experience designing reporting systems with BI tools: Power BI, Superset, Grafana, or Tableau.
• Strong knowledge of relational databases, columnar stores, object storage, data catalogs, and orchestration tools such as Airflow and dbt.
• Familiarity with compliance frameworks and regulatory requirements: GDPR, SOC 2 Type II, ISO 27001, HIPAA, PCI-DSS, and data residency.
• Experience implementing data governance controls, audit logging, access management, and encryption standards aligned to compliance mandates.

Our projects and stack

Our work spans AI products, fintech, healthcare, logistics, and B2B SaaS, usually as the engineering partner a company brings in when its data has to start carrying AI workloads and can't yet. Some engagements are a greenfield platform where you set every decision; others are the modernisation of a warehouse that has been running for a decade, where the hard part is the migration path and the compliance envelope rather than the technology.

The data side runs on Python and SQL, with Iceberg-based lakehouses, Databricks or Snowflake where the client has already chosen one, Kafka or Flink for streaming, and Airflow and dbt for orchestration and transformation. Reporting lands in Power BI, Superset, Grafana, or Tableau depending on who reads it. Infrastructure runs across Azure, AWS, GCP, and on-premises, with CI/CD and observability from day one. We tailor per engagement rather than forcing a template, and we expect this role to argue for the tailoring: new tooling has to earn its place in a system somebody will maintain for years.

Benefits that we offer

• Fully remote across Europe, with flexible hours built around a few shared core hours.
• 22 paid vacation days, plus an extra day for every year you spend with us.
• Private health insurance.
• A yearly learning budget for courses, books, and certifications, plus a conference of your choice, on us.
• Paid research time: exploring what the new models can do is work, not overtime.
• Top-tier hardware and a paid subscription to every AI tool worth having.
• Direct mentorship from senior engineers and founders who still write code.
• A referral bonus when you bring us someone great.

Our hiring process

Four steps, two to three weeks end to end, and a human answer at every one of them. You’ll know where you stand the whole way through.

01 - Intro call

Thirty minutes with an engineer, not a recruiter. We walk you through what we actually build and who for, you tell us what you want your next two years to look like, and we both decide whether it's worth going further.

02 - Technical deep-dive

Ninety minutes on real work. We pull apart a system you've shipped, then work through a problem shaped like the ones we take on. No whiteboard trivia, no algorithm quiz you'd never use again.

03 - Meet the team

A conversation with the people you'd sit beside every day, plus the founders. You ask the uncomfortable questions about deadlines, on-call, and disagreement, and get honest answers before you commit to anything.

04 - Offer

We move fast here: a decision within a few days of the last call, with the offer, the level, and the reasoning behind both laid out in writing. If it's a no, you get the actual reason.