data pipeline engineer

TowneBank

<div>
 The Data Engineer will design, build, and maintain batch ETL pipelines on a modern Databricks Lakehouse platform, delivering high-quality data solutions that support critical banking functions. You will take ownership of pipelines end-to-end, from ingestion through transformation, quality assurance, and delivery to downstream consumers, including Power BI dashboards and analytical models.
 <br/>
 <br/>
 The ideal candidate combines strong technical depth in Spark and Delta Lake with a natural orientation toward documentation, process improvement, and clear communication. This role requires the ability to work autonomously, prioritize effectively across competing demands, and contribute to the ongoing maturation of the team&rsquo;s engineering practices.
 <br/>
 <br/>
 <strong>
  Essential Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Data Pipeline Development: Design, build, and maintain batch ETL pipelines that ingest data from diverse source systems into the Databricks environment. Own the full pipeline lifecycle including ingestion, transformation, serving, monitoring, and incident resolution.
  </li>
  <li>
   Data Quality and Integrity: Implement automated validation, reconciliation checks, and data quality gates across pipelines. Ensure data meets standards for accuracy, completeness, timeliness, and consistency. Maintain historical data for auditability and compliance.
  </li>
  <li>
   Performance Optimization: Optimize data processing performance on Databricks through efficient Spark SQL, partitioning strategies, and Delta Lake table maintenance.
  </li>
  <li>
   Data Modeling for Analytics: Design dimensional models (star schema, aggregation tables) that serve Power BI dashboards and self-service analytics effectively. Prepare the semantic layer for AI-powered analytics capabilities, including Databricks Genie Rooms, through clean business logic, well-documented table relationships, and intuitive naming conventions.
  </li>
  <li>
   Process and Documentation: Establish and maintain runbooks, deployment procedures, coding standards, and operational documentation. Contribute to code review practices, automated quality checks, and repeatable processes that enable the team to scale.
  </li>
  <li>
   Governance and Compliance: Adhere to enterprise data governance policies and implement security best practices for sensitive financial data. Enforce access controls, encryption, and data lineage tracking across pipelines in accordance with banking regulations.
  </li>
  <li>
   Cross-Team Collaboration: Work with data architects, analysts, and business stakeholders to gather requirements and translate business needs into scalable data solutions. Communicate technical constraints and timelines clearly to non-technical partners.
  </li>
  <li>
   Mentoring and Knowledge Sharing: Support the development of junior and mid-level engineers through code review, pairing, and in-context coaching. Contribute to a culture of continuous learning and shared technical ownership.
  </li>
  <li>
   Continuous Improvement: Identify and implement improvements to enhance pipeline stability, efficiency, and scalability. Evaluate and adopt emerging Databricks features and industry best practices as appropriate.
  </li>
  <li>
   Adhere to applicable federal laws, rules, and regulations including those related to Anti-Money Laundering (AML) and the Bank Secrecy Act (BSA).
  </li>
  <li>
   Other duties as assigned.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Minimum Required Skills And Competencies
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience: Bachelor&rsquo;s degree in Computer Science or related field (or equivalent practical experience). 5+ years of experience as a data engineer in complex, large-scale data environments, preferably in the cloud.
  </li>
  <li>
   Databricks and Spark Proficiency: Strong hands-on expertise with Databricks and the Apache Spark ecosystem (PySpark, Spark SQL) for building and optimizing large-scale data pipelines. Production experience with Delta Lake tables and Lakehouse architectural patterns.
  </li>
  <li>
   Delta Lake Operations: Working experience with OPTIMIZE, VACUUM, Z-ordering, MERGE INTO for upserts, and time travel for debugging and auditing. Ability to articulate practical differences between Delta Lake and raw Parquet.
  </li>
  <li>
   Programming and SQL: Proficient in Python (including PySpark) for data processing. Strong SQL skills for complex querying and transformation. Emphasis on writing clean, well-structured, maintainable code.
  </li>
  <li>
   Data Modeling: Strong understanding of dimensional modeling and data warehousing concepts, including star schemas, slowly changing dimensions, and designing tables optimized for BI tool consumption (import vs. DirectQuery, pre-aggregation strategies).
  </li>
  <li>
   Pipeline Architecture: Experience designing end-to-end data pipeline architectures including orchestration, monitoring, alerting, and error handling. Familiarity with Databricks Jobs, Apache Airflow, or equivalent workflow tools.
  </li>
  <li>
   Data Quality and Testing: Hands-on experience implementing automated data validation, reconciliation checks, and quality gates in production ETL pipelines.
  </li>
  <li>
   Data Governance and Security: Knowledge of data governance standards and security best practices for managing sensitive data. Understanding of compliance requirements in banking (encryption, PII handling, auditing) and ability to enforce access controls and data lineage documentation.
  </li>
  <li>
   Version Control and CI/CD: Experience with Git-based workflows and CI/CD pipelines for deploying data pipeline code in a controlled, repeatable manner.
  </li>
  <li>
   Communication and Collaboration: Ability to translate technical concepts for non-technical audiences, document processes clearly, and collaborate effectively across engineering and business teams.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Desired Skills And Competencies
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Banking and Financial Domain Knowledge: Experience in banking, financial services, or other regulated industries. Familiarity with credit risk data, financial reconciliation, or regulatory reporting requirements.
  </li>
  <li>
   Regulatory Data Pipelines: Experience building pipelines for compliance use cases with audit trail, data lineage, and accuracy requirements (e.g., CCAR, Basel, AML reporting).
  </li>
  <li>
   Unity Catalog: Experience with Databricks Unity Catalog for data governance, access control, and catalog management.
  </li>
  <li>
   Power BI and Semantic Modeling: Familiarity with Power BI development, DAX, or semantic modeling for analytics consumption.
  </li>
  <li>
   AI/BI Analytics: Exposure to Databricks AI/BI features, including Genie Rooms for natural-language analytics, or experience preparing data layers for AI-driven consumption.
  </li>
  <li>
   Streaming Data: Exposure to real-time data streaming and event-driven architectures. Knowledge of Spark Structured Streaming or Kafka alongside batch workflows.
  </li>
  <li>
   DataOps Practices: Understanding of DataOps techniques including automated testing, monitoring, and CI/CD for data pipelines.
  </li>
  <li>
   Certifications: Relevant certifications such as Databricks Certified Data Engineer or cloud platform data engineering certifications.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Physical Requirements
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Express or exchange ideas by means of the spoken word via email and verbally.
  </li>
  <li>
   Exert up to 10 pounds of force occasionally, use your arms and legs, and sit most of the time.
  </li>
  <li>
   Have close visual acuity to perform activities such as analyzing data, viewing a computer terminal, reading, and preparing documentation.
  </li>
  <li>
   Not substantially exposed to adverse environmental conditions.
  </li>
 </ul>
</div>


Data Engineer

Purchasing Power, LLC

<div><div><div><p>Purchasing Power is a leading employee purchase program that helps people buy the products and services they need through the convenience of payroll deduction. As part of PROG Holdings (NYSE: PRG), a FinTech holding company with more than 20 years of experience, we&rsquo;re focused on building simple, responsible financial solutions that put people first. PROG Holdings&rsquo; portfolio includes Progressive Leasing, a market leader in lease‑to‑own solutions, Purchasing Power, and Four, a Buy Now Pay Later (BNPL) platform &mdash; all united by a commitment to innovation, access, and meaningful impact.</p><p>
We&rsquo;re looking for a <b>Data Engineer </b>to support the development and operation of our enterprise data platform. This role is highly tactical and execution‑focused, contributing directly to the reliability, scalability, and performance of our AWS‑based data ecosystem.</p><p>
You&rsquo;ll work closely with the Senior Director of Data Engineering and platform team to design, build, and maintain streaming and batch data pipelines, supporting real‑time ingestion, transformation, and delivery into analytics and reporting systems. This is a great opportunity for someone who enjoys working in distributed systems and wants to have a direct impact on how data is delivered across the business.</p><p><b>
WHAT YOU&rsquo;LL WORK ON</b></p><p><b>
Data Pipeline Development</b></p><ul><li>
Develop and maintain streaming and batch data pipelines using Kafka, Spark, and related technologies</li><li>
Implement ingestion pipelines using Kafka Connect and CDC patterns</li><li>
Build transformation and enrichment processes using Spark and PySpark</li><li>
Ensure reliable data delivery to downstream platforms including Greenplum, Elastic, S3, and analytics tools</li></ul><p><b>
Streaming Platform Support</b></p><ul><li>
Support Kafka topic configuration, partitioning strategies, and consumer group management</li><li>
Assist with schema registration and schema evolution practices</li><li>
Troubleshoot streaming pipeline issues and optimize performance</li><li>
Help maintain data integrity and reliability across distributed systems</li></ul><p><b>
Data Processing &amp; Transformation</b></p><ul><li>
Implement business logic and transformations within Spark processing frameworks</li><li>
Develop and maintain Airflow DAGs (or equivalent orchestration workflows)</li><li>
Build lookup and enrichment logic for streaming and batch pipelines</li><li>
Ensure data quality through validation, monitoring, and alerting practices</li></ul><p><b>
Platform Operations</b></p><ul><li>
Monitor pipeline performance, failures, and data latency</li><li>
Assist with observability and monitoring using tools such as Elastic and Kibana</li><li>
Support incident resolution and root‑cause analysis for data pipeline issues</li><li>
Contribute to system performance tuning and platform optimization</li></ul><p><b>
Collaboration</b></p><ul><li>
Work closely with the Data Engineering Manager to implement platform architecture</li><li>
Partner with analytics and reporting teams to support BI and data delivery needs</li><li>
Assist with evolving platform integrations and modernization initiatives</li></ul><p></p><p><b><br>
WHAT YOU BRING</b></p><ul><li>
3&ndash;5+ years of experience in data engineering or distributed data systems</li><li>
Hands‑on experience building and maintaining pipelines using Spark / PySpark</li><li>
Experience working with Kafka or other streaming platforms</li><li>
Strong SQL skills and experience with data transformation</li><li>
Experience with data orchestration tools such as Apache Airflow</li><li>
Familiarity with AWS‑based data environments</li><li>
Strong troubleshooting and debugging skills for data pipelines</li><li>
Curiosity, adaptability, and comfort working in evolving technical environments</li><li><p>
AI‑enabled tools are already part of how work gets done across our organization, and their use will continue to expand over time. We value people who are curious, adaptable, and open to learning as roles and workflows evolve.</p></li></ul><p></p><p><b><br>
WHY THIS ROLE IS UNIQUE</b></p><ul><li>
We act with intensity, urgency, and a passion for supporting our customers and growing our business</li><li>
We hold ourselves accountable and approach challenges with a positive, &ldquo;can‑do&rdquo; mindset</li><li>
We deliver as one team, working with integrity, trust, transparency, and respect</li><li>
We believe diversity, authenticity, and curiosity make us better and fuel innovation</li><li>
We continuously ask: Is there a better way?</li></ul><p></p><p><b><br>
WHAT WE OFFER</b></p><ul><li>
Hybrid work model (Atlanta‑based)</li><li>
Comprehensive benefits: medical, dental, vision, company‑paid Basic Life &amp; AD&amp;D</li><li>
401(k) Retirement Plan</li><li>
Flexible Paid Time Off</li><li>
Career Development opportunities</li><li>
Employee Purchase Program</li></ul></div></div></div><p></p>

Data Engineer (Hybrid - Atlanta, GA)

Zoolatech

<div><div><div><div><div><div><div><div><b>OVERVIEW</b></div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div><p>Our client is a global jewelry manufacturer that is transforming its data and analytics footprint and is also on a journey from IaaS-based Microsoft/QV solutions to Azure PaaS.</p><p>
As a Data Engineer, you will collaborate with product owners, architects, and other key profiles across the global organisation. You will help business verticals and clusters maximize the value of the data and drive business change as a result.</p><p>
Your primary focus will be building and implementing scalable data products on the global data platform together with other smart engineers in the organization.</p>
</div><div><ul><li><p>Work closely with your colleagues within a product team to design scalable, efficient, and stable products</p></li><li><p>
﻿﻿Work closely with product owners to build and deliver features designed to drive commercial business cases and efficiencies within reporting and analytics, and other data-driven use cases</p></li><li><p>
﻿﻿Create and maintain a high-quality data pipeline architecture</p></li><li><p>
﻿﻿Obtain relevant business and system knowledge to convert requirements efficiently to technical design documentation and conceptual data modelling</p></li><li><p>
﻿﻿Ingest large, complex data sets, automate manual processes, optimize data delivery, improve data quality, etc.</p></li><li><p>
﻿﻿Own your data products and track continuous stability and benefits</p></li><li><p>
Live and breathe &quot;build -once-consume-many&quot; culture in which we enable others to use our data products for maximum value-add</p></li><li><p>
﻿﻿Work with stakeholders and teams to assist with data-related technical challenges</p></li><li><p>
﻿﻿Follow best practice and existing guidelines, but also push the limits</p></li><li><p>
﻿﻿Thrive in a fast-paced environment, and you get motivated by challenges as a true problem solver.</p></li></ul>
</div><div><ul><li><p>Relevant education within Computer Science, Mathematics, Economics, Information Management, or Statistics</p></li><li><p>
﻿﻿3+ years of experience as a Data Engineer or in a Software engineering role with a focus on data</p></li><li><p>
﻿﻿Proven experience with ADF, Data Lake, Synapse, T/U-SQL, Databricks, MDS, DAX, SSIS, and SQL Server</p></li><li><p>
﻿﻿Experience with at least one object-oriented or functional/scripting language - Java, Scala, Python, etc.</p></li><li><p>
DevOps and CI/CD focus</p></li><li><p>
Hands-on with git and coding best practices</p></li><li><p>
Unit testing</p></li><li><p>
Experience in working with relational data sets using SQL</p></li><li><p>
Experience with data warehousing, dimensional modelling, and cubes</p></li><li><p>
﻿﻿Experience in building and optimizing data pipelines, architectures, and datasets</p></li><li><p>
﻿﻿Experience in working with Azure, AWS, or other cloud providers</p></li><li><p>
Designed, built, and delivered production-ready data products at an enterprise level</p></li><li><p>
Highly proficient in English and able to communicate with all levels</p></li><li><p>
﻿﻿Agile mindset and experience of Scrum</p></li></ul><p><b>
It's a plus if you have worked with:</b></p><ul><li><p>
﻿﻿Big data: Spark, Hadoop</p></li><li><p>
﻿﻿Data pipeline and workflow tools: Airflow</p></li><li><p>
﻿﻿Infrastructure: Kubernetes and Helm; we're using Airflow running on Kubernetes</p></li><li><p>
﻿﻿Stream-processing systems: Kafka or Spark Streaming</p></li></ul>
</div></div></div><div><div><div><div><p>Location:
</p><div>Istanbul, Turkey
</div></div><div><p>Seniority:
</p><div>Middle
</div></div><div><p>Technologies:
</p><div>Data
</div></div></div></div><div><div><p>Benefits:
</p><ul><li><b>Paid Vacation
</b></li><li><b>Hybrid Work (home/office)
</b></li><li><b>Sick Days
</b></li><li><b>Sport/Insurance Compensation
</b></li><li><b>Holidays Day Off
</b></li><li><b>English Classes
</b></li><li><b>Training Compensation
</b></li><li><b>Transportation compensation</b></li></ul></div></div></div></div></div></div>

Oran Inc

<div><h2 class="jobSectionHeader"><b>Role Overview</b></h2>
<p>We are seeking a highly experienced <b>Senior Data Engineer</b> to support a large-scale <b>Snowflake migration project</b> for a regulated financial environment (OCC). This role requires a hands-on expert who can independently design, develop, and support modern data pipelines while translating legacy ETL systems into scalable, production-ready Snowflake solutions.</p>
<p>The ideal candidate will bring deep expertise in Snowflake, strong SQL and pipeline engineering skills, and a proven track record of working in production environments with high accountability, auditability, and operational ownership.</p>
<p></p><h2 class="jobSectionHeader"><b>Key Responsibilities</b></h2>
<ul><li>Design, develop, and optimize <b>end-to-end data pipelines</b> using Snowflake and modern data engineering tools</li>
<li>Translate and modernize <b>legacy ETL workflows (SAS or similar)</b> into scalable Snowflake-based architectures</li>
<li>Build and maintain <b>metadata-driven ETL frameworks</b> with strong auditability, lineage, and control mechanisms</li>
<li>Write and optimize <b>complex SQL queries</b> for performance and scalability within Snowflake</li>
<li>Ensure <b>data quality, integrity, and governance</b> across all pipelines</li>
<li>Provide <b>production support</b>, including troubleshooting, root cause analysis, and issue resolution in a regulated environment</li>
<li>Collaborate with cross-functional teams including Data Architects, Analysts, and Business stakeholders</li>
<li>Implement best practices for <b>CI/CD, version control, and automated testing</b> in data pipelines</li>
<li>Participate in <b>code reviews, design discussions</b>, and continuous improvement initiatives</li>
</ul><p></p><h2 class="jobSectionHeader"><b>Required Qualifications</b></h2>
<ul><li>7+ years of experience in <b>data engineering / ETL development</b></li>
<li>Strong hands-on experience with <b>Snowflake</b> (data modeling, performance tuning, SnowSQL, Snowpipe, etc.)</li>
<li>Advanced proficiency in <b>SQL</b> and pipeline design</li>
<li>Proven experience migrating <b>legacy ETL systems (SAS, Informatica, or similar)</b> to modern cloud-based platforms</li>
<li>Experience building <b>metadata-driven frameworks</b> with logging, auditing, and control layers</li>
<li>Strong experience in <b>production support environments</b>, including monitoring, incident management, and recovery</li>
<li>Familiarity with <b>regulated environments</b> (financial services preferred) with strict data governance and compliance requirements</li>
<li>Experience with orchestration tools (e.g., Airflow, Azure Data Factory, etc.)</li>
<li>Strong problem-solving skills and ability to work <b>independently at a senior level</b></li>
</ul><p></p><h2 class="jobSectionHeader"><b>Preferred Qualifications</b></h2>
<ul><li>Experience working on <b>Snowflake migration projects</b> in large enterprises</li>
<li>Background in <b>SAS-based data environments</b></li>
<li>Knowledge of <b>data governance, lineage, and compliance frameworks</b></li>
<li>Experience with <b>cloud platforms</b> (AWS, Azure, or GCP)</li>
<li>Familiarity with <b>DevOps practices</b> in data engineering</li>
</ul><p></p><h2 class="jobSectionHeader"><b>Key Traits for Success</b></h2>
<ul><li>Ability to operate <b>independently with minimal supervision</b></li>
<li>Strong <b>ownership mindset</b> across both development and production support</li>
<li>Detail-oriented with a focus on <b>auditability and control</b></li>
<li>Comfortable working in <b>high-stakes, regulated environments</b></li>
<li>Strong communication and stakeholder collaboration skills</li>
</ul><p></p><h2 class="jobSectionHeader"><b>Summary</b></h2>
<p>This role demands a <b>true senior-level engineer</b> who can immediately contribute to both <b>Snowflake development and production support</b>, with deep expertise in <b>ETL modernization and legacy system migration</b>. Candidates must demonstrate not just conceptual knowledge, but <b>hands-on execution and ownership in complex environments</b>.</p></div>

Senior Data Engineer – Snowflake Migration- USC/GC

Deloitte

<div>Our Deloitte AI &amp; Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.<br>
<br>
<b>Work You'll Do</b><br>
<br>
The Palantir Data Engineer will design future-state, modern data architecture, pipelining, and analysis for transformation at the enterprise level using advanced cloud architectural principles. Using tools such as Palantir Foundry, they will also work with cutting-edge DevOps technologies, develop advanced analytics products, and apply data pipelining and statistical programming tools to enterprise data to advance and enable key mission outcomes.<br>
<br>
<b>The Team</b><br>
<br>
Deloitte's Government and Public Services (GPS) practice - our people, ideas, technology and outcomes-is designed for impact. Serving federal, state, &amp; local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.<br>
<br>
Our AI &amp; Data offering provides a full spectrum of solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively.<br>
<br>
<b>Qualifications</b><br>
<br>
Required:<br>
<ul><li>
Bachelor's Degree required.
</li><li>Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.</li><li>
1+ years of data science and data manipulation.</li><li>
1+ years of experience with pipeline and application development within Palantir Foundry.</li><li>
1+ years experience working with Palantir Foundry engineers.</li><li>
2 + years of experience as a Data Engineer, Or Data Scientist.
</li><li>Must be able to obtain and maintain required security clearances for the role.</li>
Ability to travel 5-25%, on average, based on the work you do and the clients and industries/sectors you serve.<br>
<li>
</li></ul><br>
<br>
Preferred:<br>
<ul><li>
2+ years of prior professional services or federal consulting experience.</li><li>
Creativity and innovation - desire to learn and apply new technologies, products, and libraries.</li><li>
Strong written and verbal communication skills.</li><li>
Strong organizational skills.</li></ul><br>
Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html<br>
<br>
#LI-MW5</div>

Palantir Data Engineer

Progressive Leasing

<div><div><b>Progressive Leasing </b>is a leading provider of in-store and e-commerce lease-to-own solutions. With more than 20 years in FinTech, we&rsquo;ve grown from start-up to industry leader by innovating, simplifying, and valuing people. We are a subsidiary of PROG Holdings (NYSE: PRG), a FinTech holding company with three business segments: Progressive Leasing, Purchasing Power (a leading employee purchase program for consumer products and services using payroll deduction), and Four, a Buy Now Pay Later (BNPL) platform.</div><div></div><div><br>
Purchasing Power is a leading employee purchase program that helps people buy the products and services they need through the convenience of payroll deduction. As part of PROG Holdings (NYSE: PRG), a FinTech holding company with more than 20 years of experience, we&rsquo;re focused on building simple, responsible financial solutions that put people first. PROG Holdings&rsquo; portfolio includes Progressive Leasing, a market leader in lease‑to‑own solutions, Purchasing Power, and Four, a Buy Now Pay Later (BNPL) platform &mdash; all united by a commitment to innovation, access, and meaningful impact.</div><div></div><div>
We&rsquo;re looking for a <b>Data Engineer</b><b> </b>to support the development and operation of our enterprise data platform. This role is highly tactical and execution‑focused, contributing directly to the reliability, scalability, and performance of our AWS‑based data ecosystem.</div><div></div><div>
You&rsquo;ll work closely with the Senior Director of Data Engineering and platform team to design, build, and maintain streaming and batch data pipelines, supporting real‑time ingestion, transformation, and delivery into analytics and reporting systems. This is a great opportunity for someone who enjoys working in distributed systems and wants to have a direct impact on how data is delivered across the business.</div><div></div><div><b>
WHAT YOU&rsquo;LL WORK ON</b></div><div><b>
Data Pipeline Development</b></div><ul><li>
Develop and maintain streaming and batch data pipelines using Kafka, Spark, and related technologies</li><li>
Implement ingestion pipelines using Kafka Connect and CDC patterns</li><li>
Build transformation and enrichment processes using Spark and PySpark</li><li>
Ensure reliable data delivery to downstream platforms including Greenplum, Elastic, S3, and analytics tools</li></ul><div><b>
Streaming Platform Support</b></div><ul><li>
Support Kafka topic configuration, partitioning strategies, and consumer group management</li><li>
Assist with schema registration and schema evolution practices</li><li>
Troubleshoot streaming pipeline issues and optimize performance</li><li>
Help maintain data integrity and reliability across distributed systems</li></ul><div><b>
Data Processing &amp; Transformation</b></div><ul><li>
Implement business logic and transformations within Spark processing frameworks</li><li>
Develop and maintain Airflow DAGs (or equivalent orchestration workflows)</li><li>
Build lookup and enrichment logic for streaming and batch pipelines</li><li>
Ensure data quality through validation, monitoring, and alerting practices</li></ul><div><b>
Platform Operations</b></div><ul><li>
Monitor pipeline performance, failures, and data latency</li><li>
Assist with observability and monitoring using tools such as Elastic and Kibana</li><li>
Support incident resolution and root‑cause analysis for data pipeline issues</li><li>
Contribute to system performance tuning and platform optimization</li></ul><div><b>
Collaboration</b></div><ul><li>
Work closely with the Data Engineering Manager to implement platform architecture</li><li>
Partner with analytics and reporting teams to support BI and data delivery needs</li><li>
Assist with evolving platform integrations and modernization initiatives</li></ul><div></div><div><b>
WHAT YOU BRING</b></div><ul><li>
3&ndash;5+ years of experience in data engineering or distributed data systems</li><li>
Hands‑on experience building and maintaining pipelines using Spark / PySpark</li><li>
Experience working with Kafka or other streaming platforms</li><li>
Strong SQL skills and experience with data transformation</li><li>
Experience with data orchestration tools such as Apache Airflow</li><li>
Familiarity with AWS‑based data environments</li><li>
Strong troubleshooting and debugging skills for data pipelines</li><li>
Curiosity, adaptability, and comfort working in evolving technical environments</li><li><div>
AI‑enabled tools are already part of how work gets done across our organization, and their use will continue to expand over time. We value people who are curious, adaptable, and open to learning as roles and workflows evolve.</div></li></ul><div></div><div><b>
WHY THIS ROLE IS UNIQUE</b></div><ul><li>
We act with intensity, urgency, and a passion for supporting our customers and growing our business</li><li>
We hold ourselves accountable and approach challenges with a positive, &ldquo;can‑do&rdquo; mindset</li><li>
We deliver as one team, working with integrity, trust, transparency, and respect</li><li>
We believe diversity, authenticity, and curiosity make us better and fuel innovation</li><li>
We continuously ask: Is there a better way?</li></ul><div></div><div><b>
WHAT WE OFFER</b></div><ul><li>
Hybrid work model (Atlanta‑based)</li><li>
Comprehensive benefits: medical, dental, vision, company‑paid Basic Life &amp; AD&amp;D</li><li>
401(k) Retirement Plan</li><li>
Flexible Paid Time Off</li><li>
Career Development opportunities</li><li>
Employee Purchase Program</li></ul><div></div><div><i>
Progressive Leasing welcomes and encourages diversity in the workplace. We do not discriminate in any aspect of employment on the basis of race, color, religion, national origin, ancestry, gender, sexual orientation, gender identity and/or expression, age, veteran status, disability, or any other characteristic protected by federal, state, or local employment discrimination laws where Progressive Leasing does business.</i></div></div>

Data Engineer - (Purchasing Power) | Hybrid - Atlanta, GA)

ibex

<div>
 We are seeking a highly motivated and responsible
 <strong>
  Data Engineer
 </strong>
 to join our dynamic team. This role requires hands-on experience in building scalable data pipelines, managing structured and unstructured databases, and supporting end-to-end data operations. The ideal candidate will bring strong problem-solving skills, a collaborative mindset, and a commitment to data excellence.
 <br/>
 <br/>
 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design, develop, and manage reliable and scalable data pipelines.
  </li>
  <li>
   Clean, validate, and transform data from multiple sources for analytics and operational use.
  </li>
  <li>
   Work with large datasets and ensure high data quality and availability.
  </li>
  <li>
   Develop and maintain ETL workflows using standard tools and scripting.
  </li>
  <li>
   Automate data extraction, transformation, and loading processes across systems.
  </li>
  <li>
   Monitor and optimize performance of data workflows for speed and efficiency.
  </li>
  <li>
   Develop, maintain, and optimize complex SQL queries, stored procedures, functions, and views.
  </li>
  <li>
   Design and implement relational database schemas and indexing strategies.
  </li>
  <li>
   Ensure data integrity, security, and compliance within SQL Server environments.
  </li>
  <li>
   Work with MongoDB for storing and querying unstructured or semi-structured data.
  </li>
  <li>
   Design document-based data models and manage performance tuning for MongoDB.
  </li>
  <li>
   Handle replication and backup strategies where applicable.
  </li>
  <li>
   Analyze complex data requirements and translate them into efficient technical solutions.
  </li>
  <li>
   Identify data-related issues, perform root-cause analysis, and deliver timely resolutions.
  </li>
  <li>
   Work closely with data analysts, software engineers, and business stakeholders to understand requirements.
  </li>
  <li>
   Participate in code reviews, team planning sessions, and cross-functional meetings.
  </li>
  <li>
   Take ownership of assigned tasks with accountability and attention to detail.
  </li>
  <li>
   Exhibit a proactive approach to delivering solutions and supporting team goals.
  </li>
  <li>
   Contribute to architectural discussions and recommend improvements based on industry best practices.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Information Systems, or a related field.
  </li>
  <li>
   2&ndash;3 years of experience in data engineering or database development roles.
  </li>
  <li>
   Strong proficiency in SQL Server (T-SQL, indexing, optimization).
  </li>
  <li>
   Hands-on experience with MongoDB or other NoSQL technologies.
  </li>
  <li>
   Familiarity with data pipeline tools and ETL development.
  </li>
  <li>
   Working knowledge of Python or similar scripting languages.
  </li>
  <li>
   Understanding of data modeling, normalization, and data warehousing concepts.
  </li>
  <li>
   Strong analytical thinking and problem-solving skills.
  </li>
  <li>
   Excellent communication and teamwork abilities.
  </li>
 </ul>
</div>


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