Data Engineering Lead - AIRLKLHV

Data Engineering Lead - AIRLKLHV NavitasPartners - 4.0 Pittsburgh, PA Job Details Full-time | Contract 1 day ago Qualifications Application Integration Data model design Databricks Data modeling Data Integration (Data management) Continuous Delivery (CD) implementation Cloud data warehouses DevOps Spark Scalable systems Snowflake SQL Machine learning Team management Data quality monitoring Quality control Scalability Technical troubleshooting support Data validation SSIS Redshift Real-time data processing implementation Senior level AI Batch data processing Informatica Leadership BigQuery Communication skills Python Cross-functional communication

Analytics Full Job Description Job Title:

Data Engineering Lead Location:

USA / Canada (Remote / Hybrid)

Job Type:

Contract / Full-Time (Based on Engagement) Overview We are seeking a highly skilled Data Engineering Lead to drive the design, development, and delivery of enterprise data pipelines that power analytics, machine learning, AI, and reporting initiatives. This role is essential for organizations transitioning from fragmented or legacy data environments to scalable, reliable, and business-ready data platforms. The ideal candidate is both hands-on and leadership-oriented, capable of guiding engineering teams while building robust data solutions that are secure, reusable, and optimized for enterprise decision-making. Responsibilities Lead the design and development of scalable ETL/ELT pipelines across cloud, on-premise, and hybrid environments Build and maintain data ingestion frameworks supporting structured, semi-structured, batch, streaming, API, and file-based data sources Collaborate with data architects, analytics teams, and business stakeholders to translate requirements into engineering solutions Implement data quality controls , validation processes, and monitoring to ensure trusted downstream data usage Lead data engineering teams through sprint planning, technical design, code reviews, and production support Optimize pipeline performance, storage design, and compute efficiency across cloud platforms Integrate legacy systems, enterprise applications, and operational data sources into modern data platforms Develop reusable frameworks, standards, and best practices for data engineering Required Qualifications Strong experience as a Senior Data Engineer or Data Engineering Lead in enterprise environments Hands-on expertise with ETL/ELT tools and technologies (SQL, Python, Spark, Airflow, dbt, Informatica, SSIS, or similar) Experience with modern data platforms such as Snowflake, Databricks, Azure Synapse, AWS Redshift, or BigQuery Solid understanding of data modeling, warehousing, and lakehouse architectures Experience supporting analytics, AI/ML, and reporting workloads Proven leadership experience managing engineers and ensuring high-quality delivery Strong problem-solving skills, especially with complex or legacy data systems Must-Have Skills Proven ability to build production-grade data pipelines supporting analytics and AI/ML use cases Hands-on leadership—ability to guide teams while actively troubleshooting and solving technical issues Strong understanding of scalable, secure, and high-performance data engineering practices Excellent collaboration and communication skills Preferred Qualifications Experience in regulated or complex industries (e.g., healthcare, finance, government, utilities) Familiarity with real-time/streaming data pipelines Knowledge of data governance, data quality, and observability tools Experience with CI/CD and DevOps practices for data engineering Work Environment Remote / Hybrid flexibility Fast-paced, collaborative, and innovation-driven team Opportunity to work on large-scale, high-impact data initiatives For more details reach at