Manager, Data Analytics and Engineering

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We are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates.Full-TimeManager

SalaryThe typical salary range for this position is $215,000 to $235,000.

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Job Details

Experience: 8+ years of experience in Data Engineering or related roles; 3+ years leading engineers (hiring, coaching, delivery accountability).
Required Skills: PythonSQLJavaKafkaAirflowData engineeringBigQueryDistributed Systems

Hold a graduate degree in Computer Science, Mathematics, or a related quantitative field.
Have 8+ years of experience in Data Engineering or related roles.
Have 3+ years of experience leading engineers, including hiring, coaching, and delivery accountability.
Bring strong distributed systems fundamentals, including scaling, reliability, and data correctness.
Have hands-on ability in Python and/or Java and proficiency with SQL and at least one programming language: Python, Java, or Scala.
Have experience with cloud data ecosystems; GCP is preferred, with AWS or Azure also listed.
Have experience with orchestration tools such as Airflow or Argo.
Understand data modeling, ETL/ELT patterns, and data reliability.
Have experience with streaming technologies such as Kafka, Flink, or Spark Streaming.
Have experience with modern data warehouses or lakehouses such as BigQuery, Snowflake, Redshift, or Iceberg.
Be familiar with analytics engineering tools such as dbt and semantic layers.
Have exposure to data governance, observability, and data quality tooling.

Lead, mentor, and develop a team of Data Engineers through 1:1s, coaching, and performance feedback.
Own hiring, onboarding, career development, and performance management for the team.
Design, develop, and operate data ingestion, transformation, and storage pipelines, including batch and streaming pipelines, and analytical models.
Ensure data systems are reliable, scalable, performant, and cost-efficient.
Guide technical designs, architecture decisions, and code quality.
Partner with platform and infrastructure teams on tooling, security, and scalability.
Translate business and analytics requirements into scalable technical solutions and deliver trusted datasets and metrics with partner teams.
Own sprint planning, prioritization, and delivery commitments; communicate progress, risks, and tradeoffs.
Establish data engineering standards, patterns, and documentation, and support data governance, data contracts, and schema management.

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