Lead I - Data Engineering

This role requires proficiency in data pipeline development including coding and testing data pipelines for ingesting wrangling transforming and joining data from various sources. Must be skilled in ETL tools such as Informatica Glue Databricks and DataProc with coding expertise in Python PySpark and SQL. Works independently and has a deep understanding of data warehousing solutions including Snowflake BigQuery Lakehouse and Delta Lake. Capable of calculating costs and understanding performance issues related to data solutions.

Outcomes:

1. Act creatively to develop pipelines and applications by selecting appropriate technical options optimizing application development maintenance and performance using design patterns and reusing proven solutions. rnInterpret requirements to create optimal architecture and design developing solutions in accordance with specifications.
2. Document and communicate milestones/stages for end-to-end delivery.
3. Code adhering to best coding standards debug and test solutions to deliver best-in-class quality.
4. Perform performance tuning of code and align it with the appropriate infrastructure to optimize efficiency.
5. Validate results with user representatives integrating the overall solution seamlessly.
6. Develop and manage data storage solutions including relational databases NoSQL databases and data lakes.
7. Stay updated on the latest trends and best practices in data engineering cloud technologies and big data tools.
8. Influence and improve customer satisfaction through effective data solutions.

Measures of Outcomes:

1. Adherence to engineering processes and standards
2. Adherence to schedule / timelines
3. Adhere to SLAs where applicable
4. # of defects post delivery
5. # of non-compliance issues
6. Reduction of reoccurrence of known defects
7. Quickly turnaround production bugs
8. Completion of applicable technical/domain certifications
9. Completion of all mandatory training requirements
10. Efficiency improvements in data pipelines (e. g. reduced resource consumption faster run times).
11. Average time to detect respond to and resolve pipeline failures or data issues.
12. Number of data security incidents or compliance breaches.

Outputs Expected:

Code Development:

1. Develop data processing code independently
   ensuring it meets performance and scalability requirements.
2. Define coding standards
   templates
   and checklists.
3. Review code for team members and peers.

Documentation:

1. Create and review templates
   checklists
   guidelines
   and standards for design
   processes
   and development.
2. Create and review deliverable documents
   including design documents
   architecture documents
   infrastructure costing
   business requirements
   source-target mappings
   test cases
   and results.

Configuration:

1. Define and govern the configuration management plan.
2. Ensure compliance within the team.

Testing:

1. Review and create unit test cases
   scenarios
   and execution plans.
2. Review the test plan and test strategy developed by the testing team.
3. Provide clarifications and support to the testing team as needed.

Domain Relevance:

1. Advise data engineers on the design and development of features and components
   demonstrating a deeper understanding of business needs.
2. Learn about customer domains to identify opportunities for value addition.
3. Complete relevant domain certifications to enhance expertise.

Project Management:

1. Manage the delivery of modules effectively.

Defect Management:

1. Perform root cause analysis (RCA) and mitigation of defects.
2. Identify defect trends and take proactive measures to improve quality.

Estimation:

1. Create and provide input for effort and size estimation for projects.

Knowledge Management:

1. Consume and contribute to project-related documents
   SharePoint
   libraries
   and client universities.
2. Review reusable documents created by the team.

Release Management:

1. Execute and monitor the release process to ensure smooth transitions.

Design Contribution:

1. Contribute to the creation of high-level design (HLD)
   low-level design (LLD)
   and system architecture for applications
   business components
   and data models.

Customer Interface:

1. Clarify requirements and provide guidance to the development team.
2. Present design options to customers and conduct product demonstrations.

Team Management:

1. Set FAST goals and provide constructive feedback.
2. Understand team members aspirations and provide guidance and opportunities for growth.
3. Ensure team engagement in projects and initiatives.

Certifications:

1. Obtain relevant domain and technology certifications to stay competitive and informed.

Skill Examples:

1. Proficiency in SQL Python or other programming languages used for data manipulation.
2. Experience with ETL tools such as Apache Airflow Talend Informatica AWS Glue Dataproc and Azure ADF.
3. Hands-on experience with cloud platforms like AWS Azure or Google Cloud particularly with data-related services (e. g. AWS Glue BigQuery).
4. Conduct tests on data pipelines and evaluate results against data quality and performance specifications.
5. Experience in performance tuning of data processes.
6. Expertise in designing and optimizing data warehouses for cost efficiency.
7. Ability to apply and optimize data models for efficient storage retrieval and processing of large datasets.
8. Capacity to clearly explain and communicate design and development aspects to customers.
9. Ability to estimate time and resource requirements for developing and debugging features or components.

Knowledge Examples:

Knowledge Examples

1. Knowledge of various ETL services offered by cloud providers including Apache PySpark AWS Glue GCP DataProc/DataFlow Azure ADF and ADLF.
2. Proficiency in SQL for analytics including windowing functions.
3. Understanding of data schemas and models relevant to various business contexts.
4. Familiarity with domain-related data and its implications.
5. Expertise in data warehousing optimization techniques.
6. Knowledge of data security concepts and best practices.
7. Familiarity with design patterns and frameworks in data engineering.

Proven experience as a data engineer, with at least 6 plus years of experience in a similar role. Good handson experience in PySpark & GCP or any other cloud Design, implement, and optimize data pipelines using Spark , Hadoop, Hive and other relevant technologies. Strong in SQL Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows. Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance. Strong problem-solving skills and the ability to work effectively in a collaborative team environment. Excellent communication skills, both written and verbal.