AWS Data Engineer

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. We

are a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of business

and technology across Banking & Payments, Wealth Management, Telecom, Hi-Tech, and Life Sciences.

We are building Incedo 4.0 - an AI-native, execution-focused, founder-led organization designed for scale, speed,

and long-term impact.

Incedo delivers ROI from AI @ Scale through the “Power of 3”:

• Deep domain expertise

• AI & Data capabilities

• Engineering & Operations excellence.

Role :- AWS Data Engineer

Location : Austin TX (Hybrid)

1. 8+ years of hands-on data engineering experience building production data pipelines, not just supporting or maintaining them.
2. Proven experience designing and owning end-to-end data pipelines across ingestion, transformation, storage, and consumption layers.
3. Strong experience with AWS-native data stack including S3, Glue (PySpark), Athena, and orchestration tools such as Airflow or Step Functions.
4. Experience building automated data quality checks into pipelines, such as validation rules, reconciliation logic, and anomaly detection.
5. Strong understanding of data modeling and storage design including partitioning strategies, file sizing, and handling of small files.
6. Deep, hands-on data modeling expertise, with solid grounding in data modeling concepts (dimensional modeling, normalization, slowly changing dimensions, fact/dimension design) and the ability to independently apply them to design data models and schemas for new datasets, including resolving grain or structural differences between source and target systems.
7. Direct, end-to-end ownership experience with data quality frameworks, defining validation rules, thresholds, and exception handling, not just operating within a framework someone else built.
8. Demonstrated experience defining testing standards for data pipelines and leading their adoption across a team.
9. Experience making architectural decisions and trade-offs, such as choosing between Glue, Lambda, EMR or container-based approaches based on use case.
10. Solid experience with PySpark and SQL including performance tuning and optimization in distributed processing environments.
11. Experience building reliable, production-grade pipelines including error handling, retries, and monitoring.
12. Experience reconciling and validating data across source and target systems to identify discrepancies and drive sign-off before production release.