Senior Manager, Data Engineering

Ring Inc; Houston (TX); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Hybrid work arrangements

Competitive salary

Leadership development

Learning opportunities

Job summary

PDI Technologies is seeking a hands‑on leader to own data ingestion, the lakehouse, and the query layer underpinning analytics and product. You will drive architecture, standardize pipelines, and set SLOs while hiring and growing a team of 10+ engineers.

Expect active design reviews, code reviews, and on‑call responsibilities in a fast‑moving environment. You will own end‑to‑end data landings, transformations, and trusted data contracts, collaborating with product and analytics to sequence the

Qualifications

• Bachelor's degree in CS/Engineering or equivalent.
• 8+ years in data engineering with 3+ years in leadership.
• Production Spark Structured Streaming at scale required.
• Deep Spark and PySpark performance tuning experience.
• Experience with a reusable ingestion framework and open table formats.

Responsibilities

• Lead, hire, and grow a data engineering team of 10+ engineers.
• Own unified ingestion framework for batch, CDC, and streaming sources.
• Own Spark Structured Streaming pipelines and Iceberg lakehouse design.
• Set dbt modeling standards, testing, and CI enforcement.
• Define freshness, latency SLOs and run a 24x7 on-call rotation.

Skills

Leadership & mentoring

Spark Structured Streaming

PySpark

Data ingestion frameworks

SQL & data modeling

Education

Bachelor's degree in Computer Science, Engineering, or equivalent

Tools

Apache Iceberg

dbt

Trino/Presto

Kafka / MSK

Job description

At PDI Technologies, we empower some of the world's leading convenience retail and petroleum brands with cutting-edge technology solutions that drive growth and operational efficiency.By “Connecting Convenience” across the globe, we empower businesses to increase productivity, make more informed decisions, and engage faster with customers through loyalty programs, shopper insights, and unmatched real-time market intelligence via mobile applications, such as GasBuddy. We’re a global team committed to excellence, collaboration, and driving real impact. Explore our opportunities and become part of a company that values diversity, integrity, and growth.

Role Overview

You will own how data gets into our platform and how it gets served back out — ingestion, the lakehouse, and the query layer underneath everything analytics and product depend on.

The problem is specific. Data arrives from CDC streams, transactional databases, event topics, partner APIs, and files, and today each source carries its own pipeline, its own failure modes, and its own on‑call story. Your mandate is to collapse that into one ingestion framework and one open lakehouse — reliable enough to publish SLOs against, fast enough to serve interactive query, and cheap enough to defend line by line.

This is a hands‑on role. You will set technical direction, hire, and grow the team — and you will also be in design reviews, in code review, and in the pipeline when a stateful stream will not recover from its checkpoint. Expect roughly half your time in technical work.

You own the full path: how data lands, the table format and its lifecycle, the transformation layer, the engines that serve it, and the SLOs on top of all of it. When a dataset is late or wrong, it is your team's phone that rings — and you are expected to have already built the thing that catches it first.

What you will do:

• Lead, hire, and grow a team of 10+ data engineers — set the technical bar through design and code review, not through status meetings.
• Own the architecture and delivery of a unified ingestion framework: one configuration-driven path for batch, CDC, and streaming sources, with schema evolution, replay and backfill, idempotency, dead-letter handling, and data contracts built into the framework rather than reimplemented per pipeline.
• Own production Spark Structured Streaming pipelines — watermarking, stateful joins and aggregations, checkpoint and restart discipline, exactly-once sinks, lag and backpressure management.
• Own the Apache Iceberg lakehouse: partition and sort strategy, file sizing and compaction, snapshot and orphan-file lifecycle, schema and partition evolution, and multi-engine interoperability.
• Set the dbt modeling standard — layering conventions, tests, contracts, CI enforcement, and lineage that stakeholders trust.
• Own Trino catalog design, workload isolation, and query performance for interactive and federated access.
• Define and meet freshness, completeness, and latency SLOs. Run a 24x7 on-call rotation with a short mean time to restore.
• Own cost: a defensible cost-per-pipeline and cost-per-dataset number, and the levers to move it.
• Partner with product, analytics, and architecture to sequence the roadmap, and bring rigor to decisions — collect the data, seek dissent, and run pilots rather than arguing from opinion

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 8+ years in data engineering, including 3+ years leading engineers as a manager or tech lead — and you are still hands-on in code and design.
• Production experience with Spark Structured Streaming at scale: state store growth, checkpoint recovery, watermark tuning, and late or out-of-order data.
• Deep Apache Spark and PySpark performance work — diagnosing and fixing skew, shuffle pressure, small-file problems, and executor memory failures on real workloads.
• Experience building or substantially owning a reusable ingestion framework serving multiple source types — not a collection of individual pipelines.
• Production experience with an open table format (Apache Iceberg preferred) including schema and partition evolution, compaction strategy, and migration from an existing format.
• Experience with dbt as a team-wide modeling standard, including testing and CI.
• Experience with Trino or Presto operations and query optimization.
• Experience running reliable, high-scale platform systems — 24x7 on-call, availability targets, and fast restoration of service

Preferred Qualifications

• Apache Flink, Kafka or MSK internals, or high-throughput stream-join design.
• CDC tooling in production (Debezium, DMS, GoldenGate, Qlik) and integrating legacy or mainframe sources into modern pipelines.
• Data contracts, catalog, and lineage tooling (DataHub, OpenMetadata, Glue, Unity).
• Iceberg REST catalog implementations and multi-engine interoperability.
• AWS, Kubernetes, and Terraform fluency — you can debug below the framework layer.
• Multi-tenant B2B data platforms with per-tenant cost attribution and isolation.
• Open-source contribution to the projects in this stack.

What Success Looks Like

• A stable, well-led SRE organization with clear ownership, career paths, and low regrettable attrition among your managers and their teams.
• Consistent, Datadog-driven observability and SLOs in place across the organization, with measurable reduction in Sev1/Sev2 incidents and mean time to detect/resolve.
• Modern, standardized infrastructure practices — GitOps delivery via Argo, IaC via Terraform/OpenTofu, and reliable CI/CD via Jenkins — adopted consistently across teams and clouds.
• A mature, blameless incident-management culture with strong postmortem follow-through.
• Strong cross-functional trust with engineering, product, and security/compliance stakeholders.

PDI is committed to offering a well-rounded benefits program, designed to support and care for you, and your family throughout your life and career. This includes a competitive salary, market-competitive benefits, and a quarterly perks program. We encourage a good work-life balance with ample time off [time away] and, where appropriate, hybrid working arrangements. Employees have access to continuous learning, professional certifications, and leadership development opportunities. Our global culture fosters diversity, inclusion, and values authenticity, trust, curiosity, and diversity of thought, ensuring a supportive environment for all.