Lead I - Software Engineering

Lead I - Software Engineering

Sr Engineer, Data designs and delivers scalable data architectures and advanced data engineering solutions across on-premises, cloud, and hybrid platforms to support enterprise analytics and customer intelligence use cases. This role partners closely with data engineers, architects, and analytics teams to analyze, design, and optimize data warehouse and analytics solutions that power personalization and data-driven decision-making within the customer data platform. The Sr Engineer provides hands-on technical leadership, mentors engineers, and drives the evolution of modern data architectures. Success is measured by solution effectiveness, scalability, performance, and growth in team capability and architectural maturity.

Design and develop advanced data engineering solutions that enable data pipelines, visualization, and analytical tools. Architect and implement scalable data platforms across on-premises, cloud, and hybrid environments. Build, optimize, and operate complex batch and real-time and near real-time streaming data pipelines. Build and maintain enterprise-scale Graph Databases that power organizational knowledge graphs and connected data experiences. Perform data wrangling, exploration, and discovery across heterogeneous data sources to generate insights. Lead development of large-scale data processing solutions using Databricks and Spark technologies. Contribute to team knowledge sharing and advancement of data engineering standards and capabilities. Mentor engineers to strengthen technical skills, delivery quality, and professional growth. Support project definition activities including estimation, planning, and scoping in partnership with management

Bachelor’s Degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience. 8+ years of hands-on experience designing, building, and supporting data engineering solutions. Strong experience developing and migrating data solutions across cloud platforms. Demonstrated technical leadership and mentoring experience. Strong analytical and problem-solving skills applied to complex data challenges. Ability to manage multiple concurrent initiatives with strong organizational and prioritization. Passion for learning and applying new data and platform technologies.

Core Data Engineering & Platforms. Advanced experience designing and building complex data pipelines using Python and SQL. Strong experience with cloud platforms and services, including Azure (Data Factory, Data Lake, Event Hub, Functions, Web Apps, Cosmos DB). Databricks with PySpark, Spark SQL, and Scala Spark, including cluster configuration, autoscaling, and performance optimization (e.g., Photon). Snowflake data warehousing, including schema design (star/snowflake), query optimization, and cost/performance tuning. Expertise in SQL, NoSQL, and relational database design and development. APIs, Streaming & Integration. Proficiency designing and consuming RESTful APIs with secure authentication and authorization (OAuth 2.0, JWT, API keys). Experience using Postman and Swagger/OpenAPI for API testing, documentation, and validation. Working knowledge of message queuing, stream processing, and highly scalable big-data data stores. Engineering Practices. Advanced knowledge of data pipeline development using Python and experience with languages such as SQL, DAX, Java, Scala, and/or Go. Experience performing root-cause analysis and using technology to solve complex business problems. Experience adopting AI-assisted development tools and building AI-powered agents or agentic workflows to automate engineering tasks, accelerate delivery, and enhance data platform capabilities. Experience with NL-to-SQL query generation using Databricks Genie and Snowflake Cortex to enable natural language access to data assets. Hands-on experience with Unity Catalog for data governance, fine-grained access control, and end-to-end data lineage. Experience developing AI-assisted dbt models, including AI-generated transformation logic, automated documentation, and intelligent testing. Familiarity with AI-powered data quality tools for automated profiling, anomaly detection, and pipeline observability Nice to Have. Experience with Iceberg table design and optimization. Experience with Unity Catalog governance and security. Cloud platform certifications such as Azure Certified Solutions Architect, Azure Cloud Practitioner, or MCSA. Experience contributing to enterprise data governance, security, or compliance initiatives. Experience with ML feature platforms (e.g., Feast, Tecton, or Databricks Feature Store) and vector database engineering (e.g., Pinecone, Weaviate, pgvector). Experience designing and operating streaming AI pipelines and applying MLOps practices to data engineering workflows. Exposure to end-to-end ML pipeline ownership and real-time AI inference plumbing in production environments.

TekWissen® Group is an equal opportunity employer supporting workforce diversity.