Remote tech & hiring glossary

An open-source workflow orchestration platform used to author, schedule, and monitor data pipelines as directed acyclic graphs (DAGs). Airflow is one of the most common pipeline orchestration tools listed in data engineering job descriptions on Stackroles.

A hybrid role bridging data engineering and data analysis. Analytics engineers transform raw data into clean, modelled datasets ready for business intelligence tools, typically using dbt. One of the fastest-growing job titles on Stackroles.

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Google Cloud's fully managed, serverless data warehouse designed for large-scale analytics. BigQuery uses a columnar storage format and supports SQL queries over petabyte-scale datasets. It is commonly listed as a required skill in data engineer and analyst roles on Stackroles.

A work arrangement where a professional is engaged for a fixed term or specific project, rather than as a permanent employee. Contract data roles on Stackroles are common for pipeline builds, migrations, and analytics projects.

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The core abstraction in Apache Airflow. A DAG defines the sequence and dependencies of tasks in a data pipeline. Each node in the graph is a task, and edges define execution order without cycles. Writing well-structured DAGs is a core data engineering skill.

A professional who queries, cleans, and interprets data to answer business questions and support decision-making. Data analysts typically work with SQL, BI tools like Looker or Tableau, and often dbt for transformations. One of the three core roles on Stackroles.

A professional who designs, builds, and maintains the infrastructure that moves and transforms data, including pipelines, warehouses, and data platforms. Data engineers commonly work with Python, Spark, Airflow, dbt, and cloud warehouses. The most common role on Stackroles.

A centralised repository that stores raw data at scale in its native format (structured, semi-structured, or unstructured). Data lakes are commonly built on object storage (S3, GCS, ADLS) and serve as the foundation of modern lakehouse architectures.

An architecture that combines the scalability of a data lake with the transactional reliability of a data warehouse. Implemented with technologies like Delta Lake, Apache Iceberg, or Apache Hudi. Increasingly common in senior data engineering job descriptions.

A professional who applies statistical modelling, machine learning, and data analysis to extract insights and build predictive models from data. Data scientists commonly use Python, PyTorch, scikit-learn, and MLflow. One of the three core roles on Stackroles.

A structured repository optimised for analytics and reporting, designed for fast query performance on large volumes of historical data. Common data warehouses include Snowflake, BigQuery, Redshift, and Databricks SQL. A foundational concept in all data roles.

An open-source transformation framework that lets data teams write modular SQL transformations, apply tests, and generate documentation. dbt is the de-facto standard for analytics engineering and is one of the most frequently required skills in Stackroles listings.

A unified analytics platform built on Apache Spark. Databricks provides managed notebooks, Delta Lake, MLflow, and a SQL warehouse. It is widely used for large-scale data engineering and machine learning workloads and appears in many senior data engineering job descriptions.

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A modern data integration pattern where raw data is first extracted from sources and loaded into a data warehouse, then transformed in-place using SQL or tools like dbt. ELT has largely replaced traditional ETL in cloud-native data stacks.

A data integration pattern where data is extracted from source systems, transformed to meet business requirements, and then loaded into a target data store. ETL pipelines are a core responsibility of data engineers.

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A centralised repository for storing, sharing, and serving machine learning features. Feature stores like Feast or Tecton ensure consistency between training and serving environments and are increasingly listed in ML engineering and senior data science roles.

A self-employed professional who works independently on a project or contract basis. Freelance data professionals typically manage their own clients, rates, and schedules. Stackroles lists freelance opportunities alongside full-time and contract roles.

A permanent employment arrangement with set hours, a regular salary, and typically benefits such as paid leave. Full-time data roles are the most common employment type listed on Stackroles.

A work arrangement where a professional splits time between working remotely and attending a physical office. Hybrid data roles on Stackroles typically specify the expected number of in-office days per week.

An open table format for large analytic datasets stored in data lakes. Iceberg provides ACID transactions, schema evolution, and time travel on object storage. It is a foundational technology in modern lakehouse architectures.

A distributed event streaming platform used to build real-time data pipelines and streaming applications. Kafka is a common skill requirement in senior data engineering and streaming architecture roles on Stackroles.

A business intelligence and data analytics platform from Google Cloud. Looker uses LookML to define semantic data models and is widely adopted by data-driven companies. Looker expertise is frequently requested in data analyst and analytics engineer roles.

An open-source platform for managing the machine learning lifecycle, including experiment tracking, model registry, and deployment. MLflow is commonly listed in data scientist and ML engineer job descriptions on Stackroles.

A collection of cloud-native, modular SaaS tools that together form a data engineering and analytics infrastructure. A typical modern data stack includes a cloud warehouse (Snowflake or BigQuery), a transformation tool (dbt), and a BI layer (Looker or Tableau). Many Stackroles roles require experience with some combination of these.

A work arrangement requiring the professional to work from a specific physical location. On-site data roles are less common than remote positions but appear on Stackroles for companies with strong in-person engineering cultures.

The automated scheduling, sequencing, and monitoring of data pipeline tasks. Common orchestration tools include Apache Airflow, Prefect, and Dagster. Orchestration skills are a core requirement for most data engineering roles on Stackroles.

A work arrangement where the professional works fewer hours than a standard full-time schedule. Part-time data roles on Stackroles often cover analytics support, BI maintenance, or model monitoring work.

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The dominant programming language in data engineering and data science. Python is used for ETL scripting, machine learning, data analysis (pandas, NumPy), pipeline orchestration, and API development. It is the single most commonly listed skill in Stackroles data job postings.

AWS's fully managed cloud data warehouse built for large-scale analytics. Redshift uses columnar storage and is tightly integrated with the AWS ecosystem. It is a common skill requirement in data engineering roles at AWS-heavy organisations.

A work arrangement where the professional works from any location. Remote is the most common work type on Stackroles, reflecting the globally distributed nature of data teams.

A cloud-based data warehouse platform known for its separation of storage and compute, multi-cluster architecture, and data sharing capabilities. Snowflake is one of the most frequently listed skills in data engineering and analytics engineering roles on Stackroles.

An open-source distributed computing engine for large-scale data processing. Spark supports batch and streaming workloads in Python (PySpark), Scala, and SQL. It is a key skill for senior data engineering roles dealing with high-volume data.

Structured Query Language; the standard language for querying and manipulating relational databases and data warehouses. SQL is required in virtually every data role on Stackroles, from entry-level analyst positions to senior data engineering roles.

A data processing paradigm that handles data continuously in real time, as opposed to batch processing at fixed intervals. Streaming architectures commonly use Kafka, Flink, or Spark Streaming. Streaming skills are increasingly required in senior data engineering roles.

A leading business intelligence and data visualisation platform. Tableau enables interactive dashboards and visual analytics without code. It is commonly required in data analyst roles and business intelligence engineering positions on Stackroles.

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