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Manager – Data Engineering – Business Intelligence
| Company | Movable Ink |
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| Location | United States |
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| Salary | $210000 – $230000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Senior |
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Requirements
- 6+ years of data engineering experience, including 2+ years in dimensional modeling and star schema design.
- 2+ years managing and mentoring data engineering teams, balancing leadership with hands-on contributions.
- Expert-level SQL skills, with deep experience in Amazon Redshift, Snowflake, or similar columnar databases.
- Strong experience with Apache Airflow for data pipeline orchestration, including scheduling, dependency management, and DAG optimization.
- Hands-on experience with ETL/ELT development, transforming raw data into structured, analysis-ready datasets using Python, dbt or other transformation frameworks.
- Proven ability to design high-performance data models, including slowly changing dimensions (SCD), fact tables, and surrogate keys for historical point-in-time analytics.
- Experience implementing and managing a Data Catalog (OpenMetadata preferred) for governance and discoverability.
- Hands-on expertise in data quality testing, monitoring, and anomaly detection frameworks.
- Proficiency in Tableau (or similar BI tools) to optimize reporting performance and self-service analytics.
- Strong ability to translate business needs into scalable, reliable data solutions for internal stakeholders and product insights.
- Excellent communication and stakeholder collaboration skills, ensuring alignment across engineering, analytics, and business teams.
Responsibilities
- Design and implement star schema data models across business domains and product analytics, ensuring dimensional modeling best practices.
- Manage a team of data engineers, including hiring, mentoring, and professional development in a collaborative, high-performing culture.
- Build ETL/ELT pipelines using Airflow that reliably populate fact and dimension tables in our Redshift environment.
- Partner with product teams and business stakeholders to translate reporting requirements into effective data models that serve both internal analytics and customer-facing features.
- Optimize data model performance for quick query response times in Tableau dashboards and product reporting interfaces.
- Reduce time to insights by optimizing data pipelines, transformation logic, and data delivery processes, ensuring stakeholders and product teams have fast, reliable access to actionable data.
- Develop data transformations that enable accurate historical reporting while supporting intra-day data updates for real-time insights. Implement robust data lineage tracking to ensure transparency, traceability, and trust in data workflows, enabling stakeholders to understand the origin, transformations, and dependencies of key business metrics.
- Implement automated data quality checks, anomaly detection, and reporting to ensure stakeholders and customers trust and rely on our datasets.
Preferred Qualifications
No preferred qualifications provided.
