Data Pipeline Engineer

Data Platform

Job Family

AU$110k

Salary

Average salary in Australia

17%

Job Growth

The number of positions relative to last year

Open Roles

Job openings on Alooba Jobs

Data Pipeline Engineers are responsible for developing and maintaining the systems that allow for the smooth and efficient movement of data within an organization. They work with large and complex data sets, building scalable and reliable pipelines that facilitate data collection, storage, processing, and analysis. Proficient in a range of programming languages and tools, they collaborate with data scientists and analysts to ensure that data is accessible and usable for business insights. Key technologies often include cloud platforms, big data processing frameworks, and ETL (Extract, Transform, Load) tools.

Role Requirements

3+ years of experience in software development, data engineering, or a related field
Proficiency in programming languages such as Python, Java, or Scala, and scripting languages like SQL
Experience with big data technologies and ETL processes
Knowledge of cloud services (AWS, Azure, GCP) and their data-related services
Familiarity with data modeling, data warehousing, and building high-volume data pipelines
Understanding of distributed systems and microservices architecture
Experience with source control tools like Git, and CI/CD practices
Strong problem-solving skills and ability to work independently
Excellent communication and collaboration skills
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes)
Knowledge of data security and privacy practices

Duties/Responsibilities

Design, develop, and maintain scalable and reliable data pipelines
Collaborate with data scientists and analysts to understand data needs
Implement automated workflows for data ingestion, processing, and distribution
Optimize data retrieval and develop dashboards for data monitoring
Ensure data quality and consistency across various data sources
Document data pipeline architecture and maintain data models
Identify and integrate new data sources to improve data systems
Conduct performance tuning and troubleshooting of data pipelines
Keep up-to-date with industry trends and advancements in data engineering
Promote best practices in data management and pipeline development
Participate in code reviews and contribute to team knowledge sharing
Support data governance and compliance initiatives

Discover how Alooba can help identify the best Data Pipeline Engineers for your team

Data Pipeline Engineer Levels

Intern Data Pipeline Engineer

An Intern Data Pipeline Engineer is a budding professional who assists in developing and maintaining the data infrastructure that allows for efficient data flow. They work under the guidance of experienced engineers, learning the ropes of data pipeline architecture, and contributing to the team's efforts.

Graduate Data Pipeline Engineer

A Graduate Data Pipeline Engineer is an entry-level professional who aids in the design, construction, and maintenance of data pipelines. They leverage their foundational knowledge in data management and programming to ensure smooth data flow, enabling organizations to derive valuable insights from their data.

Junior Data Pipeline Engineer

A Junior Data Pipeline Engineer is an emerging professional who assists in the design and maintenance of data pipelines, ensuring the smooth flow of data within the organization. They work with various data sources, implement ETL processes, and maintain data systems under the guidance of senior engineers.

Data Pipeline Engineer (Mid-Level)

A Mid-Level Data Pipeline Engineer is a vital cog in the data management machinery of an organization, designing and implementing data pipelines that enable efficient data flow. Their work ensures that data is accurately gathered, transformed, and stored for analysis and business intelligence purposes.

Senior Data Pipeline Engineer

A Senior Data Pipeline Engineer is a technical expert responsible for designing, building, and maintaining the data pipelines that allow for efficient and reliable data flow. They ensure that data is accessible, accurate, and secure, enabling organizations to leverage it for insights and decision-making.

Lead Data Pipeline Engineer

A Lead Data Pipeline Engineer takes charge of designing, building, and maintaining the data pipelines that enable efficient data flow within an organization. They possess advanced technical skills, a problem-solving mindset, and the leadership abilities required to guide a team of data engineers.

Common Data Pipeline Engineer Required Skills

Analytics Engineering

Analytics Programming

Analytics Project Management

Application Scaling Strategies Arrays

Arrays

Assertiveness

Automated Data Quality Checks

Automation Azure

Bayesian Analysis Bias

Bonferroni Correction

Business Analytics

Business Insights

Business Intelligence Architecture

Business Strategy

Cardinality

Cause & Effect Classes

Cloud Data Engineering

Cloud Platforms

Cloudera Data Platform

Control Structures CQRS

Data Engineering Infrastructure

Data Pipeline Orchestration

Data Storage Framework

Database & Storage Systems

Database Design

Database Management

Database Management Tool

Database Modeling

Database Scaling Strategies

Databricks

Dataflow

DataOps

DAX

dbt

Decision Trees

Dell Boomi Denodo

Denodo

Design Patterns

Difference in Differences

Dimension Tables

Distributed Computing

Distributed Data Processing

Distributed Event Store

Distributed SQL Query Engine

Do-While Loops Domo

English Spelling Erlang

Erlang

Error of Decomposition

ETL/ELT Processes

Event Driven Architecture

Event Streaming

Fact Tables

Feature Dependencies

Feature Stores Finance

Functional Requirements

Fuzzy Matching GDPR

Git

Google Sheets GPT

GPT

Hypothesis Testing IDE

IDE

Imputation

Incremental Loading

Indexing Strategies

Infrastructure as Code

Interactive Query Service

Internet Security

Interpersonal Communication

Knowledge Graphs Kotlin

Kotlin

Kubernetes

Lean Methodology LFS

LFS

Linked Lists Linux

Liskov Substitution Principle Lists

Log Management Loops

Measures of Central Tendency

Minimum Remaining Values

Missing Value Treatment

Mouseflow

Moving Averages

Multi-factor Authentication

Non-Functional Requirements

Open-Closed Principle

Operating Systems

Operation Analytics

Oracle Business Intelligence Enterprise Edition Plus

ORM

Pandas

Partitioned Tables

Partitioning

Percentages PHP

PHP

Programming Architectures

Programming Concepts

Prompt Engineering Pub/Sub

Quantum Machine Learning Qubole

Qubole

Query Execution Plans

Query Optimisation Queues

Recommendation Systems Redis

Relational Data Models

Relational Databases

Remote Repositories

Reporting

Requirements Gathering

Salesforce Customer 360

Serverless Architectures in Data

Serverless Computing

Signal to Noise Sisense

Solution Design SQL

SQL

Strategic Thinking Streams

Streams

String Manipulation Strings

Strings

Survivorship Bias Swift

The Big Five Personality Model

Transport Layer Security

Trend Analysis Trino

TypeScript Unix

VBA

Version Control Vertica

Visual Basic VLOOKUP

While Loop Wiki

Windows Task Scheduler

Workflow

Workflow Automation Worms

Worms

XML

YAML

Yield Analytics

Over 50,000 Candidates Can't Be Wrong

I like the way the Test is presented to me. Enough time is given to prepare for the Test. Also the questions are very clearly presented with enough time limit to answer it.

Mohammed

Analytics candidate at Asia Pacific enterprise

Overall, I found the test to be well-designed and comprehensive, effectively assessing the relevant skills and knowledge required for the position. The questions were thought-provoking and challenged me to think critically.

Sami

Senior marketing manager candidate for leading SE Asia corporate

This was a very interesting round and definitely tests our business acumen. Would be excited to see what's ahead.

Anoop

Data analytics candidate for large enterprise

Frankly, I loved the entire experience, I learned my shortcoming, giving a test like this after a while. An we know, practise and practise will make the you perfect!!

Rakesh

Senior marketing manager for travel company

Our Customers Say

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

We get a high flow of applicants, which leads to potentially longer lead times, causing delays in the pipelines which can lead to missing out on good candidates. Alooba supports both speed and quality. The speed to return to candidates gives us a competitive advantage. Alooba provides a higher level of confidence in the people coming through the pipeline with less time spent interviewing unqualified candidates.

Scott Crowe, Canva (Lead Recruiter - Data)

How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)