Senior Manager, Data Engineering & Analytics

Building and operating pipelines using AWS Glue, Lambda, SNS, S3, PySpark, and Amazon Redshift.

Developing and maintaining dbt models, Airflow workflows, data tests, and monitoring.

Designing dimensional models, star schemas, and curated analytical layers.

Using SQL and Python to investigate data, validate results, and solve production issues.

Building and reviewing dashboards and reports in Sigma, Looker, or similar BI tools.

Translating ambiguous business and customer needs into clear, scalable data solutions.

Taking business questions from discovery through metric definition, analysis, visualization, and recommendation.

Reviewing architecture, code, data models, dashboards, and analytical approaches.

Communicating findings, risks, limitations, and recommendations to technical and non-technical audiences.

Balancing strategic platform improvements with urgent operational and customer needs.

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.

5+ years of data engineering / data-platform experience.

2+ years of technical leadership experience and mentoring engineers.

Deep hands-on experience designing, building, and operating production data platforms and pipelines.

Strong experience with data architecture, ingestion, orchestration, transformation, modeling, warehousing, and performance optimization.

Advanced SQL skills and strong proficiency in Python and PySpark.

Experience with dbt, Apache Airflow, AWS Glue, or comparable tools.

Experience designing dimensional models, star schemas, and curated analytical layers.

Demonstrated ownership of data quality and reliability, including testing, monitoring, lineage, reconciliation, and operational support.

Experience building dashboards, reports, semantic layers, and self-service datasets using Sigma, Looker, or comparable platforms.

Strong backend engineering fundamentals, including APIs, distributed systems, and event-driven architecture.

Experience working directly with customers, executives, and cross-functional stakeholders.

Excellent written and verbal communication skills.

Strong ownership, urgency, judgment, resourcefulness, and follow-through.

A hands-on leadership style and willingness to personally solve difficult problems.

Ability to lead effectively across time zones.

Cloud and Data Platform: AWS, Amazon Redshift, S3, Lambda, Glue, SNS

Transformation and Orchestration: dbt, Apache Airflow, AWS Glue

Processing: Apache Spark, PySpark

Databases: DocumentDB, MongoDB, DynamoDB, or similar operational databases

Analytics and BI: Sigma, Looker, or comparable platforms

Languages: Advanced SQL, Python, PySpark; Java or another backend language is helpful

Modeling: Dimensional modeling, entity-relationship modeling, star schemas, and semantic layers

Experience with healthcare data, payer data, clinical workflows, remote patient monitoring, or care-management operations.

Working knowledge of HIPAA and secure handling of protected health information.

Experience producing customer-facing healthcare or operational reporting.

Experience leading a distributed or global team.

Experience scaling a data function or hiring data engineering and analytics talent.

Familiarity with Terraform or infrastructure as code.

Experience with data governance, metric definitions, data catalogs, or semantic-layer initiatives.

Familiarity with machine-learning techniques or platforms.