Data Engineer Sr

Why Meridian Softworks ? We’retheexperience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience. The opportunity At Meridian Softworks , you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries. We are proud of: The work we do and the innovation we drive Our values of share, care and dare A workplace culture that fosters creativity, diversity and autonomy Our borderless, global framework, which enables seamless collaboration The role As a Sr Data Engineer , you are passionate about experience innovation and eager to push the boundaries of what’s possible. You bring+5 years of experience, a growth mindset and a drive to make a lasting impact. You will thrive in this role if you are: A curious problem solver who challenges the status quo A collaborator who values teamwork and knowledge-sharing Excited by the intersection of technology, creativity and data Experienced in Agile methodologies and consulting (a plus) Role responsibilities • Lead complex data engineering workstreams across multiple business areas, source systems, domains, or stakeholder groups. • Define data engineering approaches that align business needs, source-system realities, platform constraints, transformation patterns, and downstream consumption requirements. • Translate ambiguous stakeholder needs into structured pipeline strategies, ingestion patterns, transformation designs, curated data layers, and actionable recommendations. • Guide the design and implementation of scalable ingestion, transformation, and serving patterns across cloud warehouses, lakehouses, and analytical environments. • Establish and reinforce best practices for schema design, pipeline modularity, layered data architecture, data validation, lineage awareness, scheduling discipline, error handling, and maintainable engineering patterns. • Design and improve reusable data engineering workflows that support reporting, dashboarding, data science, AI workflows, and agentic use cases. • Support team staffing, work allocation, prioritization, and delivery quality across data engineering engagements. • Manage, coach, and develop practitioners through feedback, guidance, and performance support. • Review deliverables for clarity, technical rigor, quality, consistency, and business usefulness. • Help teams improve data quality, reduce pipeline fragility, strengthen source alignment, and increase trust in downstream governed datasets. • Support data engineering patterns that improve AI and agentic readiness, including metadata-rich datasets, retrieval-supportive organization, document and chunk preparation support, governed access paths, and scalable data availability for downstream workflows. • Collaborate with Analytics Engineers, Data Analysts, Data Scientists, AI Scientists, AI Engineers, AI Platform Engineers, and Architects to align data foundations with downstream analytical, AI, and platform needs. • Contribute to hiring, onboarding, capability development, and team maturity within the data engineering practice. • Follow established governance, privacy, security, and data-quality standards across the work of the team. Must have qualifications To be considered for this role, you must meet the following essential qualifications: • Deep working knowledge of data engineering, cloud-based data platforms, and scalable pipeline design. • Strong ability to define data engineering approaches in complex business environments with multiple systems, domains, and downstream consumers. • Strong people leadership skills, including coaching, feedback, prioritization, and support for team development. • Strong understanding of ETL and ELT concepts, schema evolution, orchestration, dependency management, layered data design, and downstream data consumption needs. • Strong familiarity with SQL, Python, Spark, PySpark, and cloud-native data engineering workflows. • Strong understanding of medallion, lakehouse, warehouse, or layered architectural patterns and their role in governed data reuse. • Strong analytical and problem-solving skills across data-quality issues, performance challenges, dependency risks, and transformation design. • Working understanding of how reliable data foundations enable machine learning, AI workflows, retrieval quality, agentic systems, and governed application behavior. • Strong stakeholder management skills and the ability to communicate clearly with technical and non-technical audiences. • Ability to balance delivery quality, team workload, business urgency, and stakeholder expectations across multiple workstreams. • Strong written and verbal communication skills in English, including confide