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 significant 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.
- Hands-on 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

📌 Data Scientist (Burlington)
🏢 Audiobooks.com
📍 Burlington