Data Analytics Manager

Data Analytics Manager

Location: Houston, TX - 3 days onsite per week



Duration: 12 months



Interview: Virtual



Must Have: Lakehouse experience, Databricks

Note:

• Must be local with DL copy
• LinkedIn with location and photo (before 2023)

Job Summary

Our client is seeking an experienced Data Analytics Manager to lead the design and delivery of enterprise analytics solutions supporting operational, financial, and strategic business needs. The ideal candidate will have strong experience in data analytics, data architecture, data modeling, cloud platforms, and Lakehouse architectures , with hands-on experience using Databricks .

Experience working with energy trading, commodities, market, risk, financial, or ERP data is highly preferred.

Required Qualifications

• Strong experience in data analytics, enterprise analytics, data architecture, or a related leadership role .
• Proven Lakehouse experience is mandatory.
• Hands-on experience with Databricks and modern cloud data platforms.
• Strong understanding of conceptual, logical, and physical data modeling.
• Experience with data integration, data pipelines, data governance, metadata, lineage, and data quality.
• Experience translating business requirements into scalable data and analytics solutions.
• Strong leadership, communication, stakeholder management, and presentation skills.
• Experience working with cross-functional business and technology teams.
• Experience in the energy, oil & gas, commodities, or trading industry , particularly with trading, market, risk, financial, or ERP data.

Key Responsibilities

• Lead the design and implementation of enterprise analytics solutions supporting operational, financial, and strategic reporting needs.
• Partner with business leaders and stakeholders to translate business objectives into analytical requirements and scalable data solutions.
• Define and prioritize analytics roadmaps aligned with organizational goals and drive adoption of data products, reporting solutions, and self-service analytics.
• Design scalable enterprise data solutions supporting reporting, analytics, and advanced analytics initiatives.
• Contribute to enterprise and solution data architecture strategies across multiple business domains.
• Define architecture standards, reusable frameworks, and best practices while ensuring solutions meet enterprise security, governance, and architecture standards.
• Lead the development of conceptual, logical, and physical data models supporting analytical and operational reporting requirements.
• Partner with business teams to establish data domains, subject areas, and business-aligned data structures.
• Design data products that provide reliable, governed, reusable, and accessible datasets.
• Support the implementation and optimization of Lakehouse architectures using Databricks and cloud-native technologies .
• Collaborate with data engineering teams to define data ingestion, transformation, storage, and consumption strategies.
• Ensure Lakehouse and cloud data solutions are scalable, secure, performant, and aligned with business requirements.
• Integrate and analyze data across trading systems, operational platforms, ERP environments, and analytical applications.
• Support initiatives involving energy trading, commodities, market, risk, and financial data domains .
• Develop source-to-target mappings and end-to-end data flow documentation for enterprise initiatives.
• Establish and support processes for metadata management, data quality, data lineage, and data governance.
• Partner with business stakeholders and data stewards to improve data integrity, consistency, and trust.
• Drive implementation of data quality standards, monitoring frameworks, and governance processes.
• Lead teams of data analysts and collaborate closely with architects, engineers, and business stakeholders.
• Facilitate requirements workshops, solution reviews, architecture discussions, and roadmap planning sessions.
• Develop technical and business documentation, including architecture diagrams, data models, data dictionaries, data flows, and process documentation.
• Present analytical findings, recommendations, and solution strategies to senior leadership.