OVERALL DESCRIPTION

The Senior Director - Data and Analytics Engineering is responsible for defining how data is gathered, integrated, stored, managed, and used across SEPTA. The role leads a team of data and analytics engineers to design, develop, implement, maintain data infrastructure, and deliver insights in terms of dashboards, reports, and self-service analytics. They lead the Data & Analytics Engineering team, which is focused on creating data platforms, data pipelines, and analytical solutions across the enterprise. The role works closely with the Data Governance team to ensure compliance with policies, security standards, and regulatory requirements. This leader also works with Al/ML engineering team to build features and data sets for predictive models, AI/ML use cases.

SPECIFIC RESPONSIBILITIES

1. Designs, develops and executes a scalable, modem data platform strategy to support data and analytics initiatives.
2. Oversees data architecture, data pipelines, ETL and big data processing to ensure high performance, reliable and accurate data assets.
3. Optimizes data pipeline performance and automate processes to improve speed, reliability, and cost-efficiency.
4. Manages the development of dashboards, reports, and self-service analytics for data-driven decision making.
5. Collaborates with business teams to standardize key metrics, KPis and OKRs.
6. Ensures compliance with data governance policies, security standards, and regulatory requirements.
7. Builds, leads, and mentors a team of data and analytics engineers, ensuring they have the skills and resources needed to succeed in a demanding environment.
8. Drives the hiring, onboarding, and professional development of data and analytics engineers to support team expansion and evolving business needs.
9. Ensures alignment on data strategy and roadmaps with other technology and business leaders.
10. Drives continuous improvement initiatives to refine data and analytics engineering processes that support automation and scalability.
11. Influences others, demonstrate pier leadership skills, and work as part of a team.
12. Performs other duties as assigned.
     

QUALIFICATIONS AND EXPERIENCE

• Bachelor's degree in Computer Science, Information Technology, Data Science/Analytics, or related field required.
• 12+ years of experience in architecting and building enterprise data platforms at scale along with 5 years of cloud (AWS, Azure) data platform delivery required.
• Expertise in data modeling (relational, dimensional and non-relational) and establishing data architecture across multiple systems required.
• Extensive experience in gathering data via Change Data Capture, pub-sub, real-time streaming (Kafka, Kinesis), APis, etc., building data pipelines using ETL/ELT, Python, Spark, SQL, etc., curate data in data lakes, enterprise data warehouse, data marts and deliver data solutions for analytics and Al/ML required.
• Hands on experience with cloud data platforms like Snowflake, Databricks, AWS Redshift, Azure Synapse required.
• 5+ years of experience in driving data-driven decision making and developing data products (dashboards, self- service analytics, reports, etc.,) using BI tools (Tableau, Power Bl, Qlik, Looker, Cognos, MicroStrategy) required.
• Familiarity with data privacy regulations (e.g., GDPR, CCPA) and best practices for data security required.
• Experience working with Agile/Scrum methodologies required.
• Ability to communicate complex data concepts and engineering practices to both technical and non-technical stakeholders required.
• Excellent problem-solving skills, with a proactive and solution-oriented approach required.
• Strong leadership skills with a track record of building and developing high-performing teams required.
• Must stay abreast of the latest techniques and technologies relevant to data and analytics engineering and cloud­ based data platform.