Senior Data Engineering Manager

As a Senior Data Engineering Manager, you will lead a team responsible for the strategic development and delivery of data products. You will oversee the integration of data from various sources, guiding the evolution of our analytical data landscape, including the merging of historical data from legacy platforms with data ingested from new platforms. Your role involves directing the analysis and manipulation of large datasets, ensuring the activation of data assets to enable enterprise platforms and analytics within Google Cloud Platform. You will also lead the design and implementation of data transformation and modernization initiatives on Google Cloud Platform, ensuring the creation of scalable data pipelines that land data from source applications, integrate into subject areas, and build data marts and products for analytics solutions.

You will guide your team in conducting deep-dive analyses, including those related to Current State Receivables and Originations data within our data warehouse, performing impact analysis related to Ford Credit North America's modernization efforts and providing strategic implementation solutions. This role requires significant experience supporting North American teams. Moreover, you will lead cross-functional efforts, partnering closely with our AI, data science, and product teams to develop innovative data solutions that build the future for Ford Credit. You will coach and mentor your direct reports, fostering professional growth and technical expertise. You will also work directly with the Director of Data Engineering, based in the United States, collaborating on strategic direction and execution.

We require demonstrated leadership in designing, implementing, and operationalizing large-scale data warehouses, data lakes, and analytics platforms on Google Cloud Platform or other cloud environments. We are looking for candidates with a proven ability to lead and guide a team of data engineers, drive technical architecture decisions, and make strategic technology choices within the Google Cloud Platform ecosystem and related third-party technologies. This includes demonstrating an ability to design the right solutions with the appropriate combination of technologies for deployment on Google Cloud Platform.

Must Have Skills –

• 12+ years of technical leadership experience in delivery of Data Warehouse Solutions on-prem or on cloud for large companies, and business adoption of these platforms to build insights & analytics

• 8+ years’ experience leading technical teams, delivering complex projects using Agile methodologies, and product support for those solutions.

• Solid knowledge of cloud data architecture, data modelling principles, DevOps, Data compliance, global (North America/Europe) data protection laws, security and data governance.

• Very strong leadership and communication skills to aid customer and program teams in making data-driven decisions.

• Demonstrated experience required in engineering Data warehouse solution in Google or other cloud systems.

• Driven and managing complex projects & aggressive timelines

• Google Cloud certified professional Data Engineer

• Bachelor’s degree in- Computer science, Computer engineering, Data science or related field

• Experience on Google cloud with deep understanding and design and development experience with Google Cloud Platform products on Infrastructure, Data management, Application Development, Smart Analytics, Artificial Intelligence, Security and DevOps

• Knowledge of most of the following:

o Extract, Transform and Load (ETL) & Big Data Tools: BigQuery, Cloud Dataflow, Cloud Proc, Cloud Pub/Sub, Cloud Composer, Dataform, Data Plex, Google Looker Studio, Google Cloud Storage.

o Data Quality and Governance

o Designing, building, and deploying ML pipelines (MLOps) to solve business challenges using Python/BQML/Vertex AI on GCP. Work closely with data scientists to help deploy their models.

• Proven track of managing large global budgets, and organization change management.

• Ability to negotiate with and influence stakeholders & drive forward strategic data transformation

• Quick learner, self-starter, energetic leaders with drive to deliver results.

• Empathy and care for customers and teams, as a leader guide teams on advancement of skills, objective setting and performance assessments.
  

Good to Have Skills –

• Master of Business Administration (MBA)

• Experience in technical program management & delivering transformational projects

• Building high performance teams

• Managing/or working with globally distributed teams

• Prior experience in leveraging offshore development service providers

• Experience in a Fintech/Banking or large manufacturing company.

• Accountable for supporting the creation of the modern Datawarehouse platform in Google Cloud that enables Analytics, insights & AI/ML at speed.

• Managing stakeholders and projects, collaborating on prioritization strategies, roadmaps, architecture & features delivery

• Leading enablement of new GCP BQ capabilities & engineering patterns for enterprise-wide adoption.

• Accountable for ensuring data solutions pass banking regulatory and compliance standards.

• Provide technical leadership in design & delivery of Google cloud data platform, using agile practices and delivering continuous business value

• Drive business adoption of new scalable and reliable data platform for enterprise’s data analytics, Insights & AI/ML modeling requirements

• Collaborate with Architects and cross functional application/product teams and Ford Data Factory team to integrate data solutions required to support multiple parallel projects

• Familiarity with hands-on development of reusable solution patterns with advanced GCP tools & guide teams on how to effectively use these tools

• Be the trusted partner of business customers and engineering teams in solving technical and business problems.

• Effectively manage team’s priorities, backlogs and help remove roadblocks, to drive key business results

• Lead communication of status, issues & risks to key stakeholders

• Stay abreast on technology advancements and ensure team keeps updated on skills

· Experience leading large team of data engineers & developing talent, performance management. · Drive adoption of standardized architecture, design and quality assurance approaches across all workstreams and ensure solutions adhere to established standards.

• Leverage cloud AI/ML Platforms to deliver business and technical requirements. Manage data engineers and ML engineers that work with teams of data scientists, data analysts, and architects to successfully deliver ML/AI projects.