Senior Data Engineer

Job not on LinkedIn

🔥 14 hours ago

🏈 North America – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Zencastr

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

💼 Consulting

📣 Marketing

🛍️ eCommerce

Consulting • Marketing • eCommerce

Zencastr is a comprehensive platform designed to simplify the podcasting and video creation process. It offers high-quality audio and video recording, AI-powered editing tools, and robust hosting services. With features like remote recording, accurate transcriptions, and monetization options, Zencastr caters to podcasters, video creators, and interviewers, allowing them to produce and share engaging content effortlessly across various platforms.

📋 Description

• Build and own the data foundation powering analytics, reporting, and organizational decision-making • Design and build a conformed, Kimball-style dimensional model across operational, behavioral, and transactional data • Own ingestion end to end, including capturing historical changes from sources that do not preserve history natively • Consolidate transformation logic into a single governed and tested layer • Implement and manage the data warehouse and transformation layer • Build and maintain reliable pipelines transforming operational data into structured, analytics-ready datasets • Design and maintain dimensions, facts, and bridge tables with clearly defined grain • Develop scalable data models and data marts for reporting and business analysis • Manage and optimize data ingestion and event workflows • Implement orchestration, testing, freshness monitoring, and alerting • Build and maintain change capture or snapshotting for historical reporting and slowly-changing dimensions • Improve data freshness from batch refreshes toward near-real-time availability and establish freshness SLAs • Establish standards for data modeling, documentation, testing, governance, data quality, validation, and consistency • Encode business metric definitions to prevent reporting drift across teams • Document models and definitions for analyst and stakeholder self-service • Monitor and optimize pipeline, storage, warehouse query, performance, reliability, and cost efficiency • Collaborate with analysts, business stakeholders, engineering, and technical teams to translate requirements into scalable data solutions • Proactively improve data systems as the organization grows

🎯 Requirements

• 5+ years of experience specifically in data engineering, analytics engineering, or a closely related role, including having built and owned a dimensional model in production • Strong proficiency in SQL • Experience with document-based operational databases such as MongoDB and analytical data warehouses such as BigQuery, Snowflake, Redshift, or similar • Experience building fact and dimension tables using star schema principles, with knowledge of grain, conformed dimensions, and slowly-changing dimensions • Hands-on experience with modern transformation and modeling frameworks such as dbt, Dataform, or similar • Experience managing warehouse transformation layers with version control, testing, and CI • Experience building and maintaining reliable ETL/ELT pipelines • Experience with orchestration tooling such as Airflow, Dagster, Prefect, or similar • Experience with data ingestion or event streaming platforms such as RudderStack, Segment, Pub/Sub, or similar • Experience ensuring reliable upstream data flows, including identity stitching across web and mobile • Solid understanding of data modeling, schema design, dimensional modeling, and performance considerations • Track record of inheriting and operating systems you did not build • Strong focus on data quality, validation, and governance • Understanding of performance optimization across pipelines, storage, and warehouse queries • Ability to explain technical tradeoffs clearly to non-engineers • Comfortable executing technically and shaping data architecture standards in a growing environment