Lead data Engineer

About the Role:
Duration: 4-6 monthsLocation: RemoteTimings: Full Time (As per company timings)Notice Period: (Immediate Joiner - Only)Experience: 7+ YearsThis is a hands-on leadership role where you’ll guide the ongoing optimization of ETL infrastructure while also influencing broader platform improvements. You’ll work closely with analysts, engineers, and leadership to ensure pipelines are reliable, efficient, and structured for long-term scale. This includes helping formalize data governance practices, evolving a light semantic layer, and enabling greater downstream automation of analysis and AI use cases.You don’t need to architect the future of the entire data platform—but you will help shape its direction and make it real.What You’ll Do:Take ownership of pipeline stability and performance across the GCP-based stack (BigQuery, GCS, Dataprep/Dataflow)Lead the enhancement of existing ETL workflows to support better modularity, reusability, and error handlingHelp introduce lightweight governance practices—including column-level validation, source tracking, and transformation transparencySupport development of a semantic layer (e.g., KPI definitions, normalized metric naming) to reduce rework and support downstream usersWork with analysts and dashboard developers to structure data outputs for intuitive use and parameterizationCollaborate with team leadership to prioritize improvements based on impact and feasibilitySupport platform readiness for automated reporting, predictive modeling, and AI-enhanced analysisContribute to team culture through clear documentation, mentoring, and code reviewParticipate in hiring, onboarding, and evolving internal standardsMust-Have:7+ years of experience in data engineering, preferably in a fast-paced agency or multi-client environmentSolid command of Google Cloud Platform, especially BigQuery, GCS, and Cloud Dataprep (Alteryx) or DataflowStrong SQL and Python skills with a focus on transformation and data reliabilityExperience building and maintaining ETL pipelines in productionFamiliarity with metadata-driven development, version control, and task orchestration (Airflow or equivalent)Proven ability to balance individual execution with team collaborationClear communicator, able to translate technical trade-offs to non-technical stakeholdersNice-to-Have:Experience applying basic data governance principles (e.g., lineage tracking, validation frameworks, naming conventions)Exposure to building or maintaining a semantic layer (via dbt, LookML, etc.)Familiarity with AI/ML workflows or tooling for automated insight generationUnderstanding of marketing or media datasetsExperience developing custom marketing attribution modelsExperience mentoring junior team members or participating in code/process standardization