A remote data infrastructure engineer builds and operates the foundational data platform systems — ingestion pipelines, storage layers, orchestration frameworks, and compute infrastructure — that analytics, data science, and machine learning teams depend on for their work.
Remote data infrastructure engineer roles sit at the intersection of data engineering and platform engineering, operating at a layer below product-facing data work and focused on the reliability, scalability, and developer experience of the data platform itself.
What data infrastructure engineers do
Data infrastructure engineers design and build the systems that move, store, and serve data at scale: they architect streaming and batch ingestion pipelines that pull data from operational databases, event streams, and third-party APIs into the data warehouse or data lake; they manage the compute and storage infrastructure (Spark clusters, Trino or Presto query engines, S3 or GCS data lakes, Snowflake or BigQuery environments) that analysts and data scientists run their workloads on; and they build the orchestration and scheduling layer (Airflow, Prefect, Dagster) that coordinates pipeline execution and handles failures gracefully. They own the reliability and performance of this infrastructure: SLOs for pipeline freshness and data quality, alerting for pipeline failures, and capacity planning for growing data volumes. Data infrastructure engineers also build the internal developer platform for data teams — tooling for pipeline testing, data discovery catalogues, and developer environment setup — that determines how productive data engineers and analysts can be.
Skills and qualifications
Candidates typically have four or more years of data engineering or infrastructure engineering experience, with demonstrated depth in distributed data processing (Spark, Flink, or equivalent) and cloud data platforms (AWS, GCP, or Azure data services). Strong SQL proficiency and understanding of data warehouse design (dimensional modelling, data vault, or lakehouse patterns) is expected. Infrastructure-as-code skills (Terraform, CDK) for managing data platform resources programmatically are increasingly standard. Python proficiency for pipeline development and tooling is essential. Experience with streaming architectures (Kafka, Kinesis, Pub/Sub) alongside batch processing is expected at companies with real-time data requirements. Understanding of data governance concerns — lineage, access control, PII handling — is a growing expectation as regulatory requirements tighten.
Tools and technologies
Data infrastructure engineers work across the modern data stack: orchestration platforms (Apache Airflow, Prefect, Dagster, Mage), processing engines (Apache Spark, Flink, Trino, dbt for transformation), storage systems (S3, GCS, Delta Lake, Apache Iceberg, Apache Hudi), data warehouses (Snowflake, BigQuery, Redshift, Databricks), and streaming platforms (Kafka, Confluent, Kinesis, Pub/Sub). Data catalogues (DataHub, Amundsen, Atlan) and data quality platforms (Great Expectations, Monte Carlo, Soda) are within the data infrastructure domain. Infrastructure management uses Terraform, Helm (for Kubernetes-deployed data services), and cloud-native managed services.
Seniority levels and career path
The data infrastructure career path runs: data engineer → senior data engineer → data infrastructure engineer → staff data engineer or data platform architect → head of data infrastructure or director of data engineering. Some organisations use "data platform engineer" as an equivalent or more junior title. Data infrastructure engineers with strong platform engineering skills move into data platform leadership or general platform engineering leadership. Those with deep ML infrastructure experience move into ML platform engineering or head of ML infrastructure roles.
Compensation and salary
Remote data infrastructure engineers typically earn between $150,000 and $230,000 total compensation depending on experience and the scale of the data platform they manage. At top-tier technology companies and data-intensive businesses (fintech, e-commerce, adtech), total compensation can reach $250,000–$350,000 including equity. Data infrastructure expertise commands a premium relative to product-facing data engineering because the skills are scarcer — fewer engineers have both the data engineering and the infrastructure platform engineering depth the role requires.
Industries and employers hiring
Technology companies with significant data-driven products — e-commerce, fintech, adtech, social media, and SaaS analytics platforms — are the primary employers because their business depends on reliable, scalable data infrastructure. Data tooling companies (Databricks, Snowflake, Confluent, Monte Carlo) hire data infrastructure engineers as domain experts who work on the platforms they sell. Financial services companies hire for regulated data infrastructure with strict access control and auditability requirements. Healthcare technology companies hire for HIPAA-compliant data platforms supporting clinical analytics and ML model training.
Remote work dynamics
Data infrastructure engineering is highly compatible with remote work — pipeline development, infrastructure automation, and platform tooling work are primarily code and configuration in version-controlled repositories, with output validated through cloud-based monitoring dashboards. The primary remote consideration is on-call availability for pipeline failures or compute infrastructure incidents. Data infrastructure engineers typically participate in on-call rotations for platform reliability, which requires clear escalation paths and well-documented runbooks accessible from any location. Collaboration with data scientists and analysts across time zones requires good async data documentation and self-service platform tooling.
How to get hired
Candidates should demonstrate ownership of a data platform at scale — from architecture decisions through operational incidents. Concretise with metrics: pipeline throughput handled, data freshness SLO achieved, cost optimisation (compute or storage spend reduced by a specific percentage), or a major architecture migration completed (e.g., moving from a monolithic Airflow setup to a modular Dagster pipeline architecture). Be prepared to design a data architecture from scratch in a system design interview: how you would ingest events from a high-volume source, process them into a queryable format, and ensure data quality and freshness SLOs are met.
Frequently asked questions
What is the difference between a data infrastructure engineer and a data engineer? Data engineers typically focus on building specific pipelines and transformations for a product or analytics use case; data infrastructure engineers focus on the platform those pipelines run on — the orchestration framework, the compute layer, the storage systems, and the developer tooling. At smaller companies one person does both; at larger companies the roles are separated.
Is dbt a data infrastructure tool or a data engineering tool? dbt operates at the transformation layer — it sits on top of the data warehouse and is primarily a data engineering tool. Data infrastructure engineers manage the systems that dbt runs on (the warehouse, the orchestrator that schedules dbt runs, the CI/CD pipeline for dbt models) rather than writing dbt models themselves, though understanding dbt's operational requirements is part of the data infrastructure role.
Do data infrastructure engineers need ML experience? Not always, but ML infrastructure overlap is increasing. At companies with active ML programmes, data infrastructure engineers often own the feature store, training data pipelines, and model serving data infrastructure alongside the analytics data platform. ML infrastructure expertise is a significant differentiator for data infrastructure engineers at AI-first companies.