Flagstar -AWS + Data Engineer positions in Flagstar (4 nos)

Senior Data Engineer; Must Have: Strong hands-on AWS, Snowflake, SQL and dbt experience. Nice to have – Python, PySpark, Airflow, Informatica

Requirements and Responsibilities

What You’ll Do

Design, develop, and maintain scalable data pipelines and data transformation frameworks using AWS, Snowflake, and dbt.

Develop robust ETL/ELT pipelines to ingest, transform, and integrate data from multiple source systems into Snowflake.

Build and maintain data models in Snowflake supporting analytics, reporting, operational applications, and downstream data consumers.

Develop reusable dbt models, macros, tests, snapshots, and incremental pipelines following established data engineering standards.

Write complex and optimized SQL for data transformation, aggregation, validation, and analytical workloads.

Design and implement scalable data ingestion solutions using AWS services such as S3, Glue, Lambda, and related data services .

Develop data pipelines for both structured and semi-structured data and work with formats such as JSON, CSV, Parquet, and Avro.

Implement incremental loading, CDC, partitioning, clustering, and other optimization techniques to efficiently process large datasets.

Design and maintain appropriate data warehouse schemas and dimensional models , including fact tables, dimension tables, star schemas, and data marts.

Implement data quality and validation frameworks using dbt tests, SQL-based validation, reconciliation, and data profiling .

Monitor data pipelines and proactively identify failures, performance issues, data quality problems, and processing bottlenecks.

Troubleshoot pipeline failures and data discrepancies and perform detailed root-cause analysis .

Optimize Snowflake workloads through appropriate warehouse sizing, query optimization, clustering, partition pruning, and workload management .

Implement appropriate logging, monitoring, error handling, alerting, and recovery mechanisms for production data pipelines.

Develop automated data pipelines and workflows using orchestration tools such as AWS Glue, Airflow, or similar technologies .

Implement version control, CI/CD, and deployment processes for dbt models, SQL code, and data pipelines .

Work closely with Data Architects, Business Analysts, ETL Developers, SQL Developers, QA Engineers, and application teams to understand requirements and deliver scalable data solutions.

Participate in data architecture and technical design discussions and contribute to data platform modernization initiatives.

Support migration of legacy ETL processes to cloud-based AWS/Snowflake ELT architectures .

Develop and maintain technical documentation covering data pipelines, data models, source-to-target mappings, dependencies, and operational procedures.

Conduct code reviews and ensure adherence to development, security, performance, and data engineering standards.

Support production releases, incident management, troubleshooting, and post-production validation .

Work in an Agile/Scrum environment and actively participate in sprint planning, daily stand-ups, reviews, retrospectives, and technical discussions.

Mentor junior data engineers and contribute to knowledge sharing and continuous improvement.

What You’ll Bring

Bachelor’s degree or higher in Computer Science, Information Technology, Engineering, or a related field .

5+ years of relevant experience in data engineering, data integration, data warehousing, or cloud data platforms.

Strong hands-on experience with Snowflake as an enterprise cloud data warehouse.

Strong hands-on experience with AWS data services , particularly: Amazon S3

AWS Glue

AWS Lambda

IAM and security concepts

Other AWS data/analytics services as applicable

Strong hands-on experience with dbt (data build tool) , including: dbt models

Incremental models

Macros

Sources

Seeds

Snapshots

Tests

Documentation

Model dependencies

Advanced proficiency in SQL , including complex joins, CTEs, subqueries, window functions, aggregations, and performance optimization.

Strong understanding of ETL/ELT architecture and data pipeline development .

Strong understanding of data warehousing and dimensional modeling concepts.

Experience designing and implementing fact and dimension models, star schemas, snowflake schemas, and data marts .

Experience working with large-volume datasets and optimizing data processing workloads.

Experience with structured and semi-structured data such as JSON, CSV, Parquet, and Avro .

Strong understanding of data quality, validation, reconciliation, and data governance practices.

Experience implementing incremental loads and CDC processes.

Experience with Git and CI/CD practices for data engineering projects.

Experience with pipeline orchestration and scheduling tools such as Airflow, AWS Glue Workflows, or similar technologies .

Strong troubleshooting and root-cause-analysis skills.

Experience working in Agile/Scrum environments .

Banking or financial-services domain experience is preferred.

Experience with Python for data engineering, automation, or scripting is a plus.

Experience with PySpark / Apache Spark is a plus.

Experience with Informatica or other enterprise ETL tools is a plus, particularly for legacy ETL modernization.

Experience with data migration from traditional data warehouses to AWS/Snowflake is a plus.

Knowledge of Snowflake security, RBAC, data sharing, streams/tasks, and stored procedures is a plus.

Strong analytical and problem-solving skills.

Ability to work independently and collaborate effectively with cross-functional teams.

Strong communication skills in English, both written and verbal.

Recommended External Screening Criteria

For Senior Data Engineer – AWS/Snowflake/dbt , I would keep the following as the core mandatory screening criteria:

5+ years of Data Engineering / Data Warehousing experience

Strong hands-on Snowflake

Strong hands-on SQL

Hands-on dbt development

AWS data services experience , particularly S3 and Glue

ETL/ELT pipeline development

Data Warehousing & Dimensional Modeling

Experience with large-volume data

Data quality and validation

Git / CI/CD experience

Preferred

Python

PySpark / Spark

Airflow

Snowflake Streams & Tasks

Snowflake performance tuning

AWS Lambda

Informatica

CDC

Legacy ETL modernization

Banking / Financial Services experience