> Markdown version of [/jobs/ext/4624111-sr-data-platform-engineer-platform-lead-digital-products-_reading-pa_fulltime](https://www.wearedevelopers.com/jobs/ext/4624111-sr-data-platform-engineer-platform-lead-digital-products-_reading-pa_fulltime). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr Data Platform Engineer / Platform Lead - Digital Products _Reading,PA_Fulltime

- **Company:** Keylent Inc.
- **Location:** Reading, PA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Airflow, Amazon Web Services, Amazon S3, Confluence, Big Data, Software as a Service, Cloud Database, Cloud Storage, Continuous Delivery, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Security, Relational Databases, Database Queries, DevOps, Digital Architecture, Distributed Computing Environment, Python (Programming Language), PostgreSQL, OpenShift, Operational Databases, Query Optimization, Raw Data, Prometheus, SQL Databases, Data Streaming, Workflow Management Systems, Data Logging, Feature Engineering, Snowflake, Grafana, Apache Spark, Change Data Capture, Containerization, Pyspark, Kubernetes, Infrastructure Automation Frameworks, AWS Data Analytics, Apache Kafka, Bitbucket, Machine Learning Operations, Data Pipelines, Dynatrace, Atlassian Bamboo, Docker, Elk Stack
- **Published:** November 7, 2026
- **Apply:** https://www.dice.com/job-detail/af631509-3548-4a14-9e23-b819910f40d8

## About the Role

* Strong SQL skills, including query optimization

 * Proficient in Python and PySpark for large-scale/distributed data processing

 * Hands-on experience with a workflow orchestration tool (e.g., Apache Airflow)

 * Experience with a cloud data warehouse (e.g., Snowflake)

 * Experience with relational databases (e.g., PostgreSQL)

 * Experience with cloud data infrastructure (AWS), including cloud storage and distributed processing (e.g., S3, EMR/Spark)

 * 8+ years in data engineering with hands-on ownership of production data pipelines

 * Strong communication skills; able to explain technical tradeoffs to both technical and non-technical stakeholders

 * Good to Have

 * Familiarity with streaming or change-data-capture (CDC) ingestion patterns (e.g., Apache Kafka)

 * Experience building data quality frameworks and pipeline observability/alerting

 * Exposure to ML/analytics pipeline support (feature engineering, model-serving data prep)

 * Familiarity with monitoring and logging tools, such as Dynatrace, Prometheus, Grafana, or the ELK stack

 * Strong knowledge of DevOps principles, continuous integration, continuous delivery, and infrastructure automation

 * Hands-on experience with containerization technologies like Docker and container orchestration platforms like Kubernetes or OpenShift

 * Experience with CI/CD and collaboration tooling such as Bamboo, Bitbucket, and Confluence

 * Relevant certifications (e.g., SnowPro Core, SnowPro Advanced: Data Engineer, AWS Data Analytics)

 * Experience building or supporting SaaS products

 * Specialized experience with secure data sharing in Snowflake

## Description

* As a Data Platform Engineer/Platform Lead within Digital Products, you'll help build and run the data platform behind our fleet and telematics products - from ingesting raw data through to the datasets and views that power our customer-facing applications. You'll work closely with Data Science and product teams, balancing hands-on pipeline development with platform-level decisions on cost, reliability, and scale.
 * Responsibilities :

 * Design, build, and maintain data pipelines and orchestration workflows that move data from ingestion through transformation to serving layers

 * Write and optimize SQL and PySpark for large-scale data processing and transformation

 * Tune cloud data warehouse performance and cost (query optimization, refresh/scheduling strategy, resource sizing) as data volume and usage scale

 * Build and maintain data quality checks and pipeline monitoring/alerting to catch issues before they reach downstream consumers

 * Partner with Data Science to support feature engineering and analytics pipelines feeding ML/analytics workloads

 * Collaborate with product and engineering stakeholders to translate business needs into reliable, scalable pipeline design

 * Support incident response, root-cause analysis, and continuous improvement of the data platform

 * Document architecture and pipeline design to support onboarding of new team members

 * Contribute to platform-level decisions on architecture, tooling, and technical direction as the team and data volume scale