Job Role:- Senior Data Engineer / AI Native Engineer
Location- Sr. Louis, MO / Arlington, VA / NYC, NY / Jersey City, NJ (Hybrid minimum 3 days per week in office) :: Local candidates only
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Contract
Profiles That Typically Align Well
Senior Data Engineer Lead Data Engineer Principal Data Engineer Data Platform Engineer Senior Analytics Engineer Data Engineering Manager with active individual-contributor responsibilities
Profiles That Typically Do Not Align
Traditional ETL Developer focused primarily on Informatica / Ab Initio / DataStage without cloud-native engineering BI / Report Developer Diagram-only Data Architect DBA Hadoop Administrator
Day-to-Day Job Duties
- Design, build, optimize, and support enterprise-scale Databricks and AWS Lakehouse data pipelines using Spark and PySpark.
- Develop Databricks solutions leveraging Delta Lake, Medallion Architecture (Bronze/Silver/Gold), and Unity Catalog.
- Develop high-volume batch and streaming pipelines using PySpark, ensuring scalability, performance, reliability, data quality, and maintainability.
- Design and implement modern lakehouse engineering patterns including SCD Type 2, Change Data Capture (CDC), idempotent writes, schema evolution, and data contracts.
- Work across the AWS data ecosystem, including technologies such as S3, Glue, Lambda, EMR, Redshift, Kinesis, and related AWS services.
- Use GitHub Copilot and/or Claude Code CLI as part of the daily engineering workflow to accelerate application code, data-pipeline development, testing, debugging, optimization, and documentation.
- Rapidly develop proofs of concept and MVPs using AI-assisted development, while establishing clear paths to scalable production implementation.
Apply AI capabilities directly within the data-engineering lifecycle, including use cases such as:
- AI-assisted schema inference
- Automated data-quality rule generation
- LLM-based anomaly detection
- AI-generated PySpark transformations
- Intelligent data validation and enrichment
- Build data foundations for AI-enabled products, including feature stores, vector stores, RAG data layers, embedding pipelines, and other AI-ready data architectures.
- Design and develop high-volume streaming pipelines using technologies such as Spark Structured Streaming, Kafka, Kinesis, and Databricks Auto Loader.
- Develop and orchestrate enterprise data pipelines using technologies such as Databricks Workflows, Delta Live Tables, Airflow, and dbt.
- Implement secure and governed data-engineering practices using Unity Catalog, including catalog/schema organization, access control, lineage, governance, and data discovery.
- Lead technically complex data-engineering initiatives, drive solution design, coordinate engineering activities, and help resolve complex platform and pipeline issues.
- Partner with architects, software engineers, product owners, source-system teams, governance teams, analytics teams, AI teams, and business stakeholders.
- Remain actively hands-on with PySpark, Databricks, AWS, and software-engineering activities, rather than operating solely as a people manager or high-level data architect.
Basic Qualifications
- Minimum 8+ years of overall Data Engineering / Software Engineering experience delivering enterprise-scale data solutions.
- Minimum 5+ years of strong hands-on Data Engineering experience, including design, development, optimization, and support of production data pipelines.
- Minimum 3+ years of hands-on Databricks experience, including production experience with:
- Delta Lake
- Medallion Architecture Bronze / Silver / Gold
- Unity Catalog
- Minimum 5+ years of hands-on Spark experience, with strong expertise in PySpark specifically, rather than Scala-only Spark development.
- Minimum 3+ years of AWS data-platform experience, using services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, or related AWS technologies.
- Minimum 2+ years of experience leading complex data-engineering initiatives, technical workstreams, or engineering teams while remaining hands-on technically.
- Proven experience combining data engineering and software-engineering practices, including modular development, testing, source control, CI/CD, observability, and production support.
- Demonstrated daily hands-on use of GitHub Copilot and/or Claude Code CLI, with the ability to describe concrete examples of how these tools are used to accelerate engineering work.
- Demonstrated ability to build POCs and MVPs rapidly using AI-assisted development.
- Strong understanding of distributed data-processing principles, large-scale pipeline design, performance optimization, data quality, and production operations.
- Strong SQL skills and experience working with large enterprise datasets.
Critical Screening Requirements
Candidates should be screened out if they do not demonstrate:
- Proven experience spanning Data Engineering and Software Engineering.
- Proven experience leading complex data-engineering initiatives or teams.
- Hands-on Databricks experience including Delta Lake, Bronze/Silver/Gold Medallion Architecture, and Unity Catalog.
- High-volume Spark and PySpark experience; Scala-only Spark experience is not sufficient.
- Strong experience with the AWS data ecosystem.
- Daily hands-on use of GitHub Copilot and/or Claude Code CLI with specific examples.
- Ability to use AI-assisted development to rapidly create POCs and MVPs.
Travel
Travel as required based on project and client needs.
Degree
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related technical discipline, or equivalent professional work experience.
Nice to Have (But Not a Must)
- 2+ years of experience applying AI/Generative AI within data-engineering solutions, particularly:
- AI-assisted schema inference
- Automated data-quality rules
- LLM-based anomaly detection
- AI-generated PySpark transformations
- Experience building AI-enabled data products, including feature stores, vector stores, RAG data layers, and embedding pipelines.
- 2+ years of high-volume streaming experience using Spark Structured Streaming, Kafka, Kinesis, Auto Loader, or equivalent technologies.
- Strong implementation experience with modern lakehouse patterns including:
- SCD Type 2
- CDC
- Idempotent writes
- Schema evolution
- Data contracts
- Hands-on experience with dbt, Airflow, Databricks Workflows, and/or Delta Live Tables.
- Experience designing data platforms and pipelines supporting enterprise-scale GenAI, RAG, machine-learning, or agentic-AI solutions.
| Navya Gupta
Sr. IT Technical Recruiter 
| | | Email: Gtalk:
Phone: +1 Linkedin id:
Address: 505 Knolle Court, Saint Augustine| FL 32092 |
