Technical Program Manager, Data Lake

What You’ll Do

Program Leadership & Delivery

• Own the multi-quarter program plan for the unified Data Lake: scope, roadmap, milestones, budgets (OPEX/CAPEX), risks, and dependencies.
• Stand up and run the operating model: weekly workstream standups, cross-functional syncs, monthly steering committee, and a transparent executive status rhythm.
• Build and maintain a single-source-of-truth for delivery: program charter, RACI, RAID log, decision log, intake/triage process, and dashboards for progress/risks.
• Drive the migration plan from legacy pipelines and tools (e.g., Alteryx, Insighter) to the target stack (e.g., Snowflake, Power BI embedded via platform connectors).
• Coordinate parallel workstreams (ingestion, modeling, governance, reporting cutover) to hit time-bound deliverables with predictable quality.

Product Management & Roadmap

• Define and maintain the Data Lake program backlog, translating business use cases into technical epics, data contracts, and acceptance criteria.
• Partner with Product and Data Science teams to standardize media and product reporting packages and ensure they’re backed by governed, contract-driven data.
• Prioritize sources and domains for ingestion based on business value, client impact, and technical feasibility; establish clear go/no-go gates.
• Align with platform architecture to ensure scalable patterns for batch/stream ELT/CDC, cost control, observability, and reusability across domains.

Data Governance, Quality, and Security

• Establish practical data contracts with upstream product and business owners; define schema, SLAs, lineage, and DQ checks at ingestion.
• Stand up governance ceremonies and roles (data owners, stewards) and implement data catalog/lineage practices to improve discoverability and trust.
• Define and monitor quality KPIs (completeness, timeliness, accuracy) and drive remediation plans with accountable teams.
• Ensure data privacy, compliance, and security best practices (e.g., PII handling, role-based access, data masking) across environments.

Stakeholder Management & Change Adoption

• Serve as the connective tissue across Product, Engineering, Data Science, Media, Finance, and Client Partnership — communicating decisions, trade-offs, and timelines.
• Lead change management for reporting standardization (e.g., Media (AdTech/LBN)-based standard reports), business onboarding to the lake, and client-facing cutovers.
• Create enablement assets (runbooks, playbooks, onboarding guides) and training plans to accelerate adoption and reduce support burden.

Technical Fluency

• Partner effectively with architects and data engineers on Snowflake/BigQuery/Databricks, Azure/AWS/GCP services, orchestration (ADF/Airflow), and transformation (dbt).
• Understand ELT/CDC patterns, API/file ingestion, schema design for analytics, and BI tooling (Power BI, Looker). Write and review basic SQL for validation.
• Apply FinOps and performance/cost optimization practices (storage tiers, compute sizing, job scheduling, caching strategies).

Minimum Qualifications

• 8+ years in Program/Project/Product Management, with 5+ years leading complex data platform initiatives in a cloud environment.
• Proven delivery of cross-functional data programs involving multiple product lines and business stakeholders; strong executive communication.
• Hands-on experience with modern data stacks: one or more of Snowflake/BigQuery/Databricks; Azure Data Factory/Airflow; dbt; Kafka/Kinesis; Git/Terraform; REST/SFTP integrations.
• Strong grounding in data governance and quality practices, data contracts, catalog/lineage, and secure data access.
• Demonstrated expertise in Agile at scale (Scrum/Kanban), Jira/Confluence, dependency/risk management, and budget tracking (including CAPEX/OPEX).
• Competent SQL skills for validation/triage; fluency in reading pipeline/log artifacts and interpreting BI/semantic model requirements.

Preferred Qualifications

• Background in marketing/media data and standardized performance reporting (e.g., Media (AdTech/LBN), campaign hierarchies, Power BI embedded).
• Prior experience migrating from legacy ETL/BI ecosystems (e.g., Alteryx/Insighter/Tableau) to a lakehouse with standardized semantic layers.
• Experience establishing data domains and productizing data (SLAs, contracts, versioning, lifecycle) to accelerate downstream analytics.
• Familiarity with privacy, security, and compliance standards (e.g., RBAC/ABAC, PII governance) and enterprise SSO/permissions models for embedded analytics.
• FinOps mindset: cost observability, unit economics, and right-sizing compute/storage.

Success Metrics (What Great Looks Like)

• Sources Onboarded: number of prioritized sources/domains ingested to the lake with production-grade contracts and SLAs.
• Time-to-Data: cycle time from intake approval to governed data available for downstream consumption.
• Data Quality & Reliability: sustained improvement in DQ scorecards; incident MTTR reduction; SLA adherence for freshness and availability.
• Migration Progress: percent of targeted legacy pipelines and reports decommissioned; client reporting cutovers delivered on schedule.
• Adoption & Reuse: growth in standardized reporting/package usage; reduction in ad hoc one-off pipelines.
• Cost & Performance: measurable storage/compute cost per use case; query performance improvements aligned to agreed SLOs.

30/60/90-Day Plan

• 30 Days: Confirm program charter, governance model, and delivery rhythm. Baseline current-state architecture, sources, and reporting dependencies. Publish the first integrated roadmap/milestone plan and RAID.
• 60 Days: Land 1–2 ingestion patterns as reusable templates (contracts, lineage, observability). Execute the first cutover to standardized reporting for a targeted use case. Stand up DQ scorecards and weekly KPI review.
• 90 Days: Complete a tranche of source onboardings and at least one significant reporting migration. Retire targeted legacy jobs. Publish quarterly executive readout with outcomes, cost/performance improvements, and next-wave priorities.

Tools You’ll Use

• Jira, Confluence, Power BI; Snowflake/BigQuery/Databricks; Azure/AWS/GCP services (e.g., ADF, IAM); Airflow; dbt; Git; catalog/lineage tools.

Why This Role

This is a high-visibility opportunity to unify Ansira’s data foundation and materially improve the speed, quality, and consistency of insights across products and services. You will own the operating model, drive cross-functional alignment, and deliver tangible business outcomes through a modern, governed data platform.