Manager, Data Engineering - Business Intelligence
Movable Ink empowers marketers with scalable, omni-channel personalization through data activation and AI decisioning. The world’s most innovative brands rely on Movable Ink to maximize revenue, simplify workflow and boost marketing agility. Movable Ink is one of the fastest-growing SaaS companies in the U.S. and has been recognized by Inc. Magazine’s “Best Workplaces” (2022-2019) and Built In NYC’s “Best Places to Work” (2023-2018), as well as Inc. 5000, Crain's Fast 50, and Deloitte's Technology Fast 500. Headquartered in New York City, Movable Ink and its nearly 600 employees serve its global client base from operations throughout North America, Central America, Europe, Australia, and Japan.
Job Description
We're seeking an experienced Data Engineering Manager, Business Intelligence to lead our high-impact team in designing and implementing star schema data models that serve both internal business intelligence needs and client-facing product insights. In this role, you’ll own the data architecture, champion data governance and quality, and drive the adoption of OpenMetadata as our Data Catalog, ensuring data is well-documented, discoverable, and trusted. You’ll mentor and develop engineers while working hands-on to transform complex business data into scalable, high-performing data products. This position blends technical leadership with deep stakeholder collaboration, shaping how both internal teams and customers leverage data for strategic decision-making.
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.
Qualifications:
- 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.
The base pay range for this position is $210,000-$230,000/year, which can include additional on-target commission pay/bonus. The base pay offered may vary depending on job-related knowledge, skills, and experience. Stock options and other incentive pay may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, depending on the position ultimately offered.
We're seeking an experienced Data Engineering Manager, Business Intelligence to lead our high-impact team in designing and implementing star schema data models that serve both internal business intelligence needs and client-facing product insights. In this role, you’ll own the data architecture, champion data governance and quality, and drive the adoption of OpenMetadata as our Data Catalog, ensuring data is well-documented, discoverable, and trusted. You’ll mentor and develop engineers while working hands-on to transform complex business data into scalable, high-performing data products. This position blends technical leadership with deep stakeholder collaboration, shaping how both internal teams and customers leverage data for strategic decision-making.
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.
Qualifications:
- 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.
The base pay range for this position is $210,000-$230,000/year, which can include additional on-target commission pay/bonus. The base pay offered may vary depending on job-related knowledge, skills, and experience. Stock options and other incentive pay may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, depending on the position ultimately offered.
