Sr. Engineering Manager, Coach, Data Team

August 19

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

⏰ Full Time

🟠 Senior

👮‍♀️ Software Engineering Manager

🦅 H1B Visa Sponsor

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Speridian Technologies

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API • Artificial Intelligence • Fintech

Speridian Technologies is a global IT solutions and consulting company that helps leading enterprises tackle their most significant challenges through innovative technology solutions and services. With over 20 years of experience and a diverse client base, Speridian specializes in various sectors, delivering services such as digital strategy, security consulting, application development, cloud modernization, and productivity tools. The company is focused on leveraging next-gen technologies like AI and cloud platforms to drive operational excellence and customer satisfaction in industries including banking, healthcare, retail, and manufacturing.

1001 - 5000 employees

Founded 2003

🔌 API

🤖 Artificial Intelligence

💳 Fintech

📋 Description

• Data Platform Leadership & Architecture: Drive data platform strategy and architecture decisions for enterprise data systems. Design and oversee data pipelines, warehouses, and lake architectures. Champion data engineering best practices including data quality, governance, and documentation. Make critical technical trade-off decisions balancing data freshness, accuracy, and infrastructure costs • Business Ownership & Financial Accountability: Own business metrics and ROI for data platform investments and initiatives. Develop and track cost-benefit analyses for data infrastructure and tooling decisions. Manage team budget including cloud data costs, tooling, and infrastructure spend. Translate data engineering work into business value and analytical capabilities for stakeholders. Drive efficiency improvements in data processing costs while maintaining data quality • People Management & Development: Manage, mentor, and develop a team of 10-20 data engineers. Conduct regular 1:1s focused on career development and performance. Execute performance management including promotions, improvement plans, and difficult conversations. Build diverse, inclusive teams through thoughtful hiring and team composition • Data Engineering Excellence & Quality: Establish and maintain standards for data quality, pipeline reliability, and monitoring. Drive continuous improvement in ETL/ELT practices and data tooling. Ensure appropriate data governance, security, and compliance implementation. Implement metrics and monitoring for data pipeline performance and data quality • Cross-functional Partnership: Partner with Analytics, Data Science, and Business Intelligence teams on requirements. Collaborate with Product Management on data product roadmap and prioritization. Work with Software Engineering teams on application data integration. Communicate data architecture concepts and trade-offs to non-technical stakeholders • Talent Strategy & Team Building: Lead technical interviews and hiring decisions for data engineering roles. Develop team skills through mentoring, training, and stretch assignments. Identify and cultivate future data platform leaders. Build team culture emphasizing data quality, automation, and continuous learning

🎯 Requirements

• Proven track record managing data engineering teams of 20+ members • Experience owning P&L or budget responsibility for data platforms or products • Demonstrated ability to connect data infrastructure to business outcomes and ROI • Experience building and operating production data platforms at scale • Strong background in modern data engineering practices and cloud data technologies • Demonstrated ability to make architectural decisions for data systems and pipelines • Experience with full data lifecycle from ingestion through consumption • Track record of developing data engineers and building strong data engineering cultures • Bachelor's degree in Computer Science, Engineering, or equivalent experience • Technical Data Platforms: Snowflake, Databricks, BigQuery, Redshift, or similar • Data Processing: Apache Spark, Airflow, dbt, Kafka, streaming architectures • Cloud & Infrastructure: AWS/Azure/GCP data services and infrastructure as code • Data Modeling: Dimensional modeling, data vault, data mesh principles • Languages: SQL, Python, Scala, and data-specific programming paradigms • Business & Financial: Cloud data cost optimization, budget ownership, and ROI analysis • Business Metrics: Defining and tracking data platform KPIs and usage metrics • Value Communication: Articulating data investments in business terms • Resource Planning: Capacity planning for data workloads and storage • Vendor Management: Evaluating and managing data tools and platform services • Leadership: Performance management, career development, and difficult conversations • Team Building: Hiring, onboarding, and creating inclusive team environments • Communication: Technical and non-technical stakeholder management • Decision Making: Data-driven decisions balancing multiple constraints • Strategic Thinking: Aligning data platform efforts with organizational goals • Change Management: Leading teams through platform migrations and tool adoptions • Business Acumen: Understanding business context and impact of data platform decisions

🏖️ Benefits

• Purpose that matters • Teammates who care deeply • Work-life balance (no nights, weekends, or burnout) • Fully remote work