Senior Manager, Data Engineering

Staritas• Remote, US,

17h•$150,000 - $175,000•Remote

About The Position

Staritas is seeking a Senior Manager of Data Engineering to lead its enterprise data engineering function, including data platform strategy, roadmap execution, modernization, production operations, data quality, governance, and team development. This role is ideal for a hands-on data leader who can scale modern data platforms, improve reliability, guide technical standards, and partner across Technology, Product, Analytics, Security, Operations, and business teams to deliver trusted, secure, and business-ready data capabilities.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Engineering, Mathematics, or related field; equivalent experience considered.
10+ years of technology experience, including data engineering, data architecture, analytics engineering, integration, or enterprise data platform delivery.
5+ years leading data engineers, analytics engineers, architects, technical leads, or similar teams.
Proven experience delivering enterprise data platforms, production data operations, technical standards, and stakeholder-facing roadmaps.
Strong experience with cloud data platforms, data warehouses, data lakes/lakehouses, data marts, APIs, integrations, and production support.
Hands-on experience building ELT/ETL pipelines.
Experience with Informatica or comparable enterprise data integration platforms.
Strong SQL skills and experience with relational, NoSQL, columnar, and analytical databases.
Experience with AWS, Azure, Google Cloud, or hybrid cloud environments.
Knowledge of orchestration, transformation, data modeling, CI/CD, testing, observability, documentation, agile delivery, governance, metadata, lineage, data quality, and secure access.

Nice To Haves

Experience with Informatica IDMC, PowerCenter, Data Quality, or Cloud Data Integration.
Familiarity with tools such as Spark, PySpark, Airflow, dbt, Databricks, Snowflake, BigQuery, Redshift, Azure Data Factory, AWS Glue, Power BI, GitHub, GitLab, Jira, or Confluence.
Experience modernizing legacy ETL, fragmented data ecosystems, and manual reporting processes.
Experience supporting AI, machine learning, predictive analytics, or advanced analytics use cases.
Experience in regulated, compliance-oriented, SaaS, ERP, CRM, healthcare, financial, or analytics environments.

Responsibilities

Lead and develop a high-performing data engineering team.
Own the data engineering roadmap, delivery planning, technical standards, and platform reliability.
Partner with the CTO and technology leaders on data strategy, modernization, architecture, governance, and operating model.
Deliver scalable ELT/ETL pipelines, data warehouses, data lakes, marts, semantic layers, curated datasets, and analytical data products.
Establish standards for data modeling, orchestration, testing, documentation, code review, CI/CD, monitoring, and production support.
Improve data quality, observability, lineage, alerting, incident response, and DataOps practices.
Reduce technical debt, duplicated datasets, manual reporting, fragile integrations, and recurring production issues.
Support AI, machine learning, advanced analytics, and self-service analytics with trusted, governed, and secure data foundations.
Promote a culture of ownership, accountability, collaboration, documentation, and continuous improvement.