Senior Data Engineer

Responsibilities

• Design, build, and optimize ELT-based data pipelines that are reliable, scalable, and aligned to business goals
• Own and evolve data architecture (data lake/warehouse/lakehouse patterns) including ingestion, transformation, and serving layers
• Architect and implement end-to-end data solutions from source systems to curated analytics‑ready datasets
• Build and maintain data models (dimensional modeling, curated marts) to support reporting, BI, and advanced analytics
• Develop and operationalize data products (reusable datasets, semantic layers, curated tables/views, standardized metrics) for analysts/data scientists
• Improve data quality through validation, testing, monitoring, lineage, and alerting
  ; drive data reliability and SLAs
• Partner closely with data science/ML engineers on feature engineering, training datasets, and production‑ready pipelines
• Establish and enforce data engineering standards
  : coding practices, reviews, documentation, naming conventions, and pipeline design patterns
• Lead complex projects: scope, roadmap, technical direction, risk management, and cross‑team coordination
• Evaluate and introduce emerging tools/processes to improve productivity, cost, and performance (e.g., orchestration, transformation, observability)
• Mentor junior engineers and promote a culture of reuse, scalability, operational excellence, and knowledge sharing

• 7+ years of hands‑on data engineering experience (senior/lead level ownership)
• Strong expertise in SQL (advanced joins, window functions, performance tuning, query optimization)
• Proven experience building scalable pipelines using modern DE tools such as:
• Spark/Databricks (distributed processing, performance tuning, partitioning, job optimization)
• DBT (modular transformations, tests, documentation, environments)
• Python within the OSS data ecosystem (pandas/py Spark, APIs, automation, packaging)
• Solid experience with data warehouses/distributed databases such as Snowflake/Redshift (or similar) including modeling and transformations, load strategies, clustering/partitioning, and cost/performance considerations
• Strong understanding of data architecture & scalability
  : distributed systems concepts, batch vs. streaming patterns, storage formats (Parquet/Delta), and data lifecycle management
• Proficiency with software engineering fundamentals
  :
  Git workflows, CI/CD pipelines, code reviews, testing strategies, and tools like JIRA
• Comfortable in Linux environments and scripting with Bash/Z shell
• Hands‑on experience with cloud data platforms (e.g.,
  Azure
  )—compute, storage, security, networking basics for data workloads
• Strong critical thinking and problem‑solving skills—can troubleshoot pipeline failures, data issues, performance bottlenecks, and ambiguous requirements
• Ability to lead/mentor and collaborate cross‑functionally with BI, ML, analysts, and solution architects

• Familiarity with BI tools like Power BI
  , Tableau
  , Looker
  , Alteryx
• Knowledge of data governance, metadata/lineage, and observability practices
• Exposure to ML concepts (feature engineering, datasets, model handoffs)