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Job summary

The role involves developing and maintaining ETL data pipelines and analytics-ready datasets using AWS services and Python. You will also build business-critical KPIs and provide analytical insights across customer lifecycle, marketing, and revenue data.

Job details

Data Scientist / Analytics Engineer We are seeking a hands-on Data Scientist / Analytics Engineer to join our team for a 12-month maternity leave contract. This role combines data engineering, analytics, business intelligence, and data science. The successful candidate will maintain and develop data pipelines, transform large and complex datasets, build and support business-critical KPIs and reporting, and provide analytical insights across customer lifecycle, subscription, engagement, marketing, and revenue data. The ideal candidate is comfortable working independently from business requirements through data investigation, development, validation, and final reporting. Key Responsibilities Develop, maintain, and troubleshoot ETL/data pipelines using Python, PySpark, SQL, and AWS Glue. Process large datasets from relational databases, APIs, cloud platforms, marketing systems, and application/event data. Build and maintain analytics-ready datasets using Amazon S3, AWS Glue, Glue Data Catalog/Crawlers, Workflows, and Amazon Athena. Write complex SQL and PySpark transformations involving joins, window functions, aggregations, deduplication, date/time logic, and large historical datasets. Maintain incremental data processing workflows, checkpoints, partitioned datasets, and recurring production jobs. Develop and maintain customer lifecycle analytics, including free trials, active subscriptions, cancellations, suspensions, reactivations, churn, retention, and winback analysis. Analyze customer engagement and application/session activity and connect behavioral data with subscription history. Develop and maintain weekly and monthly KPIs, cohort analyses, customer segmentation, and performance reporting. Integrate and analyze marketing acquisition and advertising data across platforms, channels, campaigns, and geographies. Develop and maintain datasets and metrics used in Power BI reporting. Investigate discrepancies, data quality issues, schema changes, pipeline failures, and unexpected changes in business metrics. Maintain documentation for important datasets, business rules, KPI definitions, and data transformations. Continue to implement a data governance strategy. Collaborate to plan data requirements for new projects to ensure no gaps in measuring outcomes. Required Qualifications 3+ years of experience in Data Science, Analytics Engineering, Data Engineering, or a similar hands-on data role. Advanced SQL skills, including complex joins, CTEs, aggregations, and window functions. Strong Python skills for data processing, automation, and ETL. Handson experience with PySpark / Apache Spark. Experience working with AWS data services, particularly AWS Glue, Amazon S3, and Amazon Athena. Experience designing, maintaining, and troubleshooting production data pipelines. Experience with Power BI Service, Desktop and Dax studio Experience with Dax language and M Language in Power query. Strong understanding of data transformation, validation, data quality, and analytical dataset design. Ability to work with large datasets and troubleshoot complex data issues independently. Strong analytical and problem-solving skills. Experience with Git/version control. Ability to translate business questions into technical requirements and measurable KPIs. Strong communication skills and ability to work with both technical and business stakeholders. Nice to Have Qualifications Experience with churn, retention, cohort, customer lifecycle, LTV, or engagement analysis. Experience with marketing and attribution platforms such as Google Ads, Apple Search Ads, Adjust, CJ, or similar platforms. Experience integrating data through REST APIs. Familiarity with additional AWS services such as Lambda, RDS/Aurora, IAM, or Lake Formation. Experience with customer segmentation, RFM analysis, statistical modeling, or predictive analytics. Experience with Looker and Google services. Ideal Candidate The ideal candidate is a technically strong and detail-oriented data professional who is comfortable owning a problem end-to-end. They should be equally comfortable troubleshooting a data pipeline, writing complex SQL or PySpark transformations, investigating a KPI discrepancy, integrating a new data source, and explaining results to business stakeholders. Because this role supports existing production data processes and business-critical reporting, strong hands-on SQL, Python/PySpark, and AWS experience is particularly important. WORKING WITH US At Audiobooks.com, you'll never feel tied to your desk. We employ a hybrid work policy, working 3 days a week from home (Tuesdays & Thursdays in office). We've got two stories of really bright, open space in a great location that's just a 10minute walk from Appleby GO Station, and we offer free parking. We provide our employees with healthy snacks, full benefits, and an unlimited paid vacation policy. Our close-knit group runs a fun book club and frequent lunch outings for our resident foodies. Everyone who works here is friendly, talented, and passionate, and enjoys getting their hands dirty building amazing products. Audiobooks.com is an equal opportunity employer. We celebrate diversity and are committed to fostering an inclusive and accessible workplace where all employees feel valued and respected. We are committed to providing accommodations throughout the recruitment and selection process in accordance with applicable human rights and accessibility legislation. If you require an accommodation during any stage of the recruitment process, please let us know, and we will work with you to meet your needs.

What you’ll do

The role involves developing and maintaining ETL data pipelines and analytics-ready datasets using AWS services and Python. You will also build business-critical KPIs and provide analytical insights across customer lifecycle, marketing, and revenue data.

Requirements

Candidates must have 3+ years of experience in data science or engineering with advanced proficiency in SQL, Python, and PySpark. Experience with AWS data services and Power BI is essential for managing production data processes and reporting.

Benefits

• Full benefits • Unlimited paid vacation policy • Healthy snacks • Book club • Lunch outings

Listed skills

• Power BI · Preferred
• SQL · Preferred
• Git · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• PySpark
• SQL
• AWS Glue
• Amazon S3
• Amazon Athena
• ETL
• Data pipelines
• Power BI
• DAX
• M Language
• Data modeling
• Data governance
• Git
• KPI development
• Customer lifecycle analytics
• Business Metrics
• Customer Segmentation
• Pipelines
• Performance Reporting
• Workflow Management
• Apple Search Ads
• Data Analysis Expressions (DAX)
• Go (Programming Language)
• Git (Version Control System)
• Technical Requirements
• Application Programming Interface (API)
• Amazon Web Services
• Analytics
• Automation
• Business Intelligence
• Business Requirements
• Version Control
• Communication
• Data Processing
• Customer Engagement
• Customer Lifecycle Management
• Data Engineering
• Data Governance
• Extract Transform Load (ETL)
• Data Transformation
• Data Quality
• Relational Databases
• Engagement Marketing
• Google Services
• Marketing
• Problem Solving
• Python (Programming Language)
• Key Performance Indicators (KPIs)
• Loan-To-Value Ratios

Job areas

• Data & Analytics
• Technology
• Software
• Marketing
• Engineering
• Data Analytics Scientist
• Data Analyst
• Statistical, Mathematical and Related Associate Professionals
• Business Intelligence Analysts
• Data Scientists